diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/collection.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/collection.json index 96f4a7161..8dc9adef2 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/collection.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/collection.json @@ -11,107 +11,107 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/USGSHABs1.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/procBlanchardMonod.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_lasso.json" + "href": "./models/procCTMIMonod.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/procEppleyNorbergMonod.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/procEppleyNorbergSteele.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/procHinshelwoodMonod.json" }, { "rel": "item", "type": "application/json", - "href": "./models/USGSHABs1.json" + "href": "./models/procHinshelwoodSteele.json" }, { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/tg_ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/procBlanchardMonod.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/procCTMIMonod.json" + "href": "./models/tg_lasso.json" }, { "rel": "item", "type": "application/json", - "href": "./models/procEppleyNorbergMonod.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/procEppleyNorbergSteele.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/procHinshelwoodMonod.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", "type": "application/json", - "href": "./models/procHinshelwoodSteele.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/tg_temp_lm_all_sites.json" }, { "rel": "parent", diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/USGSHABs1.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/USGSHABs1.json index 8f8a00608..f99f61ce4 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/USGSHABs1.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/USGSHABs1.json @@ -31,7 +31,7 @@ "properties": { "title": "USGSHABs1", "description": "All forecasts for the Daily_Chlorophyll_a variable for the USGSHABs1 model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BLWA, TOMB, FLNT.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-12T00:00:00Z", "end_datetime": "2024-03-09T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/cb_prophet.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/cb_prophet.json index 2fbd1a183..a391bebc8 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/cb_prophet.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/cb_prophet.json @@ -55,7 +55,7 @@ "properties": { "title": "cb_prophet", "description": "All forecasts for the Daily_Chlorophyll_a variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-10T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/climatology.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/climatology.json index 7cf836086..2b064c2a3 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/climatology.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/climatology.json @@ -59,7 +59,7 @@ "properties": { "title": "climatology", "description": "All forecasts for the Daily_Chlorophyll_a variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: BARC, BLWA, FLNT, SUGG, TOMB, CRAM, LIRO, PRPO, PRLA, TOOK, USGS-01427510, USGS-01463500, USGS-05543010, USGS-05553700, USGS-05558300, USGS-05586300, USGS-14181500, USGS-14211010, USGS-14211720.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-09-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json index 332b92abe..57160e93f 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json @@ -59,7 +59,7 @@ "properties": { "title": "persistenceRW", "description": "All forecasts for the Daily_Chlorophyll_a variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, BARC, BLWA, CRAM, FLNT.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procBlanchardMonod.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procBlanchardMonod.json index 0e1fb94f8..9fb4cbe7c 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procBlanchardMonod.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procBlanchardMonod.json @@ -47,7 +47,7 @@ "properties": { "title": "procBlanchardMonod", "description": "All forecasts for the Daily_Chlorophyll_a variable for the procBlanchardMonod model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-13T00:00:00Z", "end_datetime": "2024-03-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procCTMIMonod.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procCTMIMonod.json index d7618e5a6..0096a9077 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procCTMIMonod.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procCTMIMonod.json @@ -47,7 +47,7 @@ "properties": { "title": "procCTMIMonod", "description": "All forecasts for the Daily_Chlorophyll_a variable for the procCTMIMonod model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-13T00:00:00Z", "end_datetime": "2024-03-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergMonod.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergMonod.json index d2ac206ff..96385b935 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergMonod.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergMonod.json @@ -47,13 +47,13 @@ "properties": { "title": "procEppleyNorbergMonod", "description": "All forecasts for the Daily_Chlorophyll_a variable for the procEppleyNorbergMonod model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-13T00:00:00Z", "end_datetime": "2024-03-06T00:00:00Z", "providers": [ { - "url": "https://projects.ecoforecast.org/neon4cast-ci/", - "name": "NEON Ecological Forecasting Project", + "url": null, + "name": "Abigail Lewis", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergSteele.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergSteele.json index aa5a87b03..674a7c6ac 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergSteele.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergSteele.json @@ -47,7 +47,7 @@ "properties": { "title": "procEppleyNorbergSteele", "description": "All forecasts for the Daily_Chlorophyll_a variable for the procEppleyNorbergSteele model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-13T00:00:00Z", "end_datetime": "2024-03-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodMonod.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodMonod.json index 443eb5052..866f073e9 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodMonod.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodMonod.json @@ -47,13 +47,13 @@ "properties": { "title": "procHinshelwoodMonod", "description": "All forecasts for the Daily_Chlorophyll_a variable for the procHinshelwoodMonod model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-13T00:00:00Z", "end_datetime": "2024-03-06T00:00:00Z", "providers": [ { - "url": null, - "name": "Abigail Lewis", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodSteele.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodSteele.json index a8423c8be..7b98da524 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodSteele.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodSteele.json @@ -47,7 +47,7 @@ "properties": { "title": "procHinshelwoodSteele", "description": "All forecasts for the Daily_Chlorophyll_a variable for the procHinshelwoodSteele model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-13T00:00:00Z", "end_datetime": "2024-03-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json index cb06a59ab..167644467 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json @@ -59,13 +59,13 @@ "properties": { "title": "tg_arima", "description": "All forecasts for the Daily_Chlorophyll_a variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Gregory Harrison", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json index 5b165538b..7e0ab949a 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_ets", "description": "All forecasts for the Daily_Chlorophyll_a variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm.json index fd8e8af79..97cc98cf9 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm.json @@ -14,10 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -149.6106, - 68.6307 - ], [ -82.0084, 29.676 @@ -53,19 +49,23 @@ [ -88.1589, 31.8534 + ], + [ + -149.6106, + 68.6307 ] ] }, "properties": { "title": "tg_humidity_lm", - "description": "All forecasts for the Daily_Chlorophyll_a variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: TOOK, BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_Chlorophyll_a variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ { - "url": "https://projects.ecoforecast.org/neon4cast-ci/", - "name": "NEON Ecological Forecasting Project", + "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", + "name": "Abigail Lewis", "roles": [ "producer", "processor", @@ -90,7 +90,6 @@ "chla", "Daily", "P1D", - "TOOK", "BARC", "BLWA", "CRAM", @@ -99,7 +98,8 @@ "PRLA", "PRPO", "SUGG", - "TOMB" + "TOMB", + "TOOK" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm_all_sites.json index 1b6f5bcdb..936fe994a 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm_all_sites.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All forecasts for the Daily_Chlorophyll_a variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_lasso.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_lasso.json index 9723e9ec0..59d1e266a 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_lasso.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_lasso.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_lasso", "description": "All forecasts for the Daily_Chlorophyll_a variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm.json index bcff228bb..3acd64daa 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_precip_lm", "description": "All forecasts for the Daily_Chlorophyll_a variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm_all_sites.json index 5bdbcbece..39b999578 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm_all_sites.json @@ -59,13 +59,13 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All forecasts for the Daily_Chlorophyll_a variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-19T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "name": "Gregory Harrison", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_randfor.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_randfor.json index c5f84b6fb..442d6780f 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_randfor.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_randfor", "description": "All forecasts for the Daily_Chlorophyll_a variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json index 052d29cfd..d0990f17a 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json @@ -14,6 +14,10 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -82.0084, + 29.676 + ], [ -87.7982, 32.5415 @@ -49,17 +53,13 @@ [ -149.6106, 68.6307 - ], - [ - -82.0084, - 29.676 ] ] }, "properties": { "title": "tg_tbats", - "description": "All forecasts for the Daily_Chlorophyll_a variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, BARC.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_Chlorophyll_a variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ @@ -90,6 +90,7 @@ "chla", "Daily", "P1D", + "BARC", "BLWA", "CRAM", "FLNT", @@ -98,8 +99,7 @@ "PRPO", "SUGG", "TOMB", - "TOOK", - "BARC" + "TOOK" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm.json index 57a89c96c..f64b679f4 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_temp_lm", "description": "All forecasts for the Daily_Chlorophyll_a variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm_all_sites.json index 08ac00311..287825cee 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm_all_sites.json @@ -59,13 +59,13 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All forecasts for the Daily_Chlorophyll_a variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/collection.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/collection.json index 94e33928f..ad935e536 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/collection.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/collection.json @@ -11,82 +11,82 @@ { "rel": "item", "type": "application/json", - "href": "./models/air2waterSat_2.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/tg_temp_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/GLEON_lm_lag_1day.json" + "href": "./models/tg_ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_lasso.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/air2waterSat_2.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/GLEON_lm_lag_1day.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_lasso.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json index b9e3cb8c2..da8f95a86 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json @@ -31,7 +31,7 @@ "properties": { "title": "AquaticEcosystemsOxygen", "description": "All forecasts for the Daily_Dissolved_oxygen variable for the AquaticEcosystemsOxygen model. Information for the model is provided as follows: Used a Bayesian Dynamic Linear Model using the fit_dlm function from the ecoforecastR package.\n The model predicts this variable at the following sites: BARC, WLOU, ARIK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-04-03T00:00:00Z", "end_datetime": "2024-08-04T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/GLEON_lm_lag_1day.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/GLEON_lm_lag_1day.json index d8bc92f79..4cb6fd85e 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/GLEON_lm_lag_1day.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/GLEON_lm_lag_1day.json @@ -47,7 +47,7 @@ "properties": { "title": "GLEON_lm_lag_1day", "description": "All forecasts for the Daily_Dissolved_oxygen variable for the GLEON_lm_lag_1day model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-02-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/air2waterSat_2.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/air2waterSat_2.json index 6e5b594a4..c5d2ef72f 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/air2waterSat_2.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/air2waterSat_2.json @@ -155,13 +155,13 @@ "properties": { "title": "air2waterSat_2", "description": "All forecasts for the Daily_Dissolved_oxygen variable for the air2waterSat_2 model. Information for the model is provided as follows: The air2water model is a linear model fit using the function lm() in R and uses air temperature as\na covariate.\n The model predicts this variable at the following sites: TOOK, WALK, WLOU, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ { "url": "https://github.com/rqthomas/neon4cast-example/blob/main/forecast_model.R", - "name": "Quinn Thomas", + "name": "Freya Olsson", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/cb_prophet.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/cb_prophet.json index ff3b55925..c241dc06d 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/cb_prophet.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/cb_prophet.json @@ -147,7 +147,7 @@ "properties": { "title": "cb_prophet", "description": "All forecasts for the Daily_Dissolved_oxygen variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, ARIK, BARC, BIGC, BLDE.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-10T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/climatology.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/climatology.json index 2d6e3764c..848595d4b 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/climatology.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/climatology.json @@ -15,24 +15,12 @@ "type": "MultiPoint", "coordinates": [ [ - -102.4471, - 39.7582 - ], - [ - -82.0084, - 29.676 - ], - [ - -119.2575, - 37.0597 - ], - [ - -110.5871, - 44.9501 + -87.7982, + 32.5415 ], [ - -96.6242, - 34.4442 + -147.504, + 65.1532 ], [ -105.5442, @@ -115,20 +103,32 @@ 39.8914 ], [ - -88.1589, - 31.8534 + -102.4471, + 39.7582 ], [ - -87.7982, - 32.5415 + -82.0084, + 29.676 ], [ - -89.4737, - 46.2097 + -119.2575, + 37.0597 ], [ - -147.504, - 65.1532 + -110.5871, + 44.9501 + ], + [ + -96.6242, + 34.4442 + ], + [ + -88.1589, + 31.8534 + ], + [ + -89.4737, + 46.2097 ], [ -89.7048, @@ -154,8 +154,8 @@ }, "properties": { "title": "climatology", - "description": "All forecasts for the Daily_Dissolved_oxygen variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, WALK, WLOU, TOMB, BLWA, CRAM, CARI, LIRO, PRPO, PRLA, TOOK, OKSR.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_Dissolved_oxygen variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: BLWA, CARI, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, TOMB, CRAM, LIRO, PRPO, PRLA, TOOK, OKSR.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-09-26T00:00:00Z", "providers": [ @@ -186,11 +186,8 @@ "oxygen", "Daily", "P1D", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", + "BLWA", + "CARI", "COMO", "CUPE", "FLNT", @@ -211,10 +208,13 @@ "TECR", "WALK", "WLOU", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", "TOMB", - "BLWA", "CRAM", - "CARI", "LIRO", "PRPO", "PRLA", diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json index 30773d536..91569c336 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json @@ -75,7 +75,7 @@ "properties": { "title": "hotdeck", "description": "All forecasts for the Daily_Dissolved_oxygen variable for the hotdeck model. Information for the model is provided as follows: Uses a hot deck approach: - Take the latest observation/forecast. - Past observations from around the same window of the season are collected. - Values close to the latest observation/forecast are collected. - One of these is randomly sampled. - Its \"tomorrow\" observation is used as the forecast. - Repeat until forecast at step h..\n The model predicts this variable at the following sites: BARC, SUGG, KING, BLDE, BIGC, MCRA, REDB, SYCA, CRAM, LIRO, PRIN, POSE, MAYF, LEWI.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-04-05T00:00:00Z", "end_datetime": "2024-09-21T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json index 8d524a047..99857b319 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json @@ -55,44 +55,52 @@ 33.751 ], [ - -102.4471, - 39.7582 + -87.4077, + 32.9604 ], [ - -82.0084, - 29.676 + -96.443, + 38.9459 ], [ - -119.2575, - 37.0597 + -122.1655, + 44.2596 ], [ - -110.5871, - 44.9501 + -149.143, + 68.6698 + ], + [ + -78.1473, + 38.8943 ], [ -96.6242, 34.4442 ], [ - -83.5038, - 35.6904 + -87.7982, + 32.5415 ], [ - -77.9832, - 39.0956 + -105.9154, + 39.8914 ], [ - -89.7048, - 45.9983 + -102.4471, + 39.7582 ], [ - -121.9338, - 45.7908 + -82.0084, + 29.676 ], [ - -87.4077, - 32.9604 + -119.2575, + 37.0597 + ], + [ + -110.5871, + 44.9501 ], [ -119.0274, @@ -110,14 +118,6 @@ -84.2793, 35.9574 ], - [ - -87.7982, - 32.5415 - ], - [ - -105.9154, - 39.8914 - ], [ -84.4374, 31.1854 @@ -135,27 +135,27 @@ 39.1051 ], [ - -96.443, - 38.9459 + -83.5038, + 35.6904 ], [ - -122.1655, - 44.2596 + -77.9832, + 39.0956 ], [ - -149.143, - 68.6698 + -89.7048, + 45.9983 ], [ - -78.1473, - 38.8943 + -121.9338, + 45.7908 ] ] }, "properties": { "title": "persistenceRW", - "description": "All forecasts for the Daily_Dissolved_oxygen variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: CARI, COMO, CRAM, CUPE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, ARIK, BARC, BIGC, BLDE, BLUE, LECO, LEWI, LIRO, MART, MAYF, TECR, TOMB, TOOK, WALK, BLWA, WLOU, FLNT, GUIL, HOPB, KING, MCDI, MCRA, OKSR, POSE.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_Dissolved_oxygen variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: CARI, COMO, CRAM, CUPE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, MAYF, MCDI, MCRA, OKSR, POSE, BLUE, BLWA, WLOU, ARIK, BARC, BIGC, BLDE, TECR, TOMB, TOOK, WALK, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ @@ -196,30 +196,30 @@ "REDB", "SUGG", "SYCA", + "MAYF", + "MCDI", + "MCRA", + "OKSR", + "POSE", + "BLUE", + "BLWA", + "WLOU", "ARIK", "BARC", "BIGC", "BLDE", - "BLUE", - "LECO", - "LEWI", - "LIRO", - "MART", - "MAYF", "TECR", "TOMB", "TOOK", "WALK", - "BLWA", - "WLOU", "FLNT", "GUIL", "HOPB", "KING", - "MCDI", - "MCRA", - "OKSR", - "POSE" + "LECO", + "LEWI", + "LIRO", + "MART" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json index c71b8e184..7065b84e0 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json @@ -155,13 +155,13 @@ "properties": { "title": "tg_arima", "description": "All forecasts for the Daily_Dissolved_oxygen variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Gregory Harrison", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json index 7ba00b87b..99a8628e4 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_ets", "description": "All forecasts for the Daily_Dissolved_oxygen variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm.json index 8ab96b344..94bdbd622 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm.json @@ -155,13 +155,13 @@ "properties": { "title": "tg_humidity_lm", "description": "All forecasts for the Daily_Dissolved_oxygen variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ { - "url": "https://projects.ecoforecast.org/neon4cast-ci/", - "name": "NEON Ecological Forecasting Project", + "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", + "name": "Abigail Lewis", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm_all_sites.json index e1aaf42c6..d88c289b1 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm_all_sites.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All forecasts for the Daily_Dissolved_oxygen variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_lasso.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_lasso.json index 85220b02c..bed0d68be 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_lasso.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_lasso.json @@ -14,6 +14,42 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -122.1655, + 44.2596 + ], + [ + -149.143, + 68.6698 + ], + [ + -78.1473, + 38.8943 + ], + [ + -97.7823, + 33.3785 + ], + [ + -99.1139, + 47.1591 + ], + [ + -99.2531, + 47.1298 + ], + [ + -111.7979, + 40.7839 + ], + [ + -82.0177, + 29.6878 + ], + [ + -111.5081, + 33.751 + ], [ -119.0274, 36.9559 @@ -113,49 +149,13 @@ [ -96.443, 38.9459 - ], - [ - -122.1655, - 44.2596 - ], - [ - -149.143, - 68.6698 - ], - [ - -78.1473, - 38.8943 - ], - [ - -97.7823, - 33.3785 - ], - [ - -99.1139, - 47.1591 - ], - [ - -99.2531, - 47.1298 - ], - [ - -111.7979, - 40.7839 - ], - [ - -82.0177, - 29.6878 - ], - [ - -111.5081, - 33.751 ] ] }, "properties": { "title": "tg_lasso", - "description": "All forecasts for the Daily_Dissolved_oxygen variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_Dissolved_oxygen variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -186,6 +186,15 @@ "oxygen", "Daily", "P1D", + "MCRA", + "OKSR", + "POSE", + "PRIN", + "PRLA", + "PRPO", + "REDB", + "SUGG", + "SYCA", "TECR", "TOMB", "TOOK", @@ -210,16 +219,7 @@ "LIRO", "MART", "MAYF", - "MCDI", - "MCRA", - "OKSR", - "POSE", - "PRIN", - "PRLA", - "PRPO", - "REDB", - "SUGG", - "SYCA" + "MCDI" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm.json index 16acb731f..771004717 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_precip_lm", "description": "All forecasts for the Daily_Dissolved_oxygen variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm_all_sites.json index 8e4a1db29..f3902f5dc 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm_all_sites.json @@ -14,22 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -88.1589, - 31.8534 - ], - [ - -149.6106, - 68.6307 - ], - [ - -84.2793, - 35.9574 - ], - [ - -105.9154, - 39.8914 - ], [ -102.4471, 39.7582 @@ -149,19 +133,35 @@ [ -119.0274, 36.9559 + ], + [ + -88.1589, + 31.8534 + ], + [ + -149.6106, + 68.6307 + ], + [ + -84.2793, + 35.9574 + ], + [ + -105.9154, + 39.8914 ] ] }, "properties": { "title": "tg_precip_lm_all_sites", - "description": "All forecasts for the Daily_Dissolved_oxygen variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_Dissolved_oxygen variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-19T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "name": "Gregory Harrison", "roles": [ "producer", "processor", @@ -186,10 +186,6 @@ "oxygen", "Daily", "P1D", - "TOMB", - "TOOK", - "WALK", - "WLOU", "ARIK", "BARC", "BIGC", @@ -219,7 +215,11 @@ "REDB", "SUGG", "SYCA", - "TECR" + "TECR", + "TOMB", + "TOOK", + "WALK", + "WLOU" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_randfor.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_randfor.json index 381e9cc7e..a1b7273cc 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_randfor.json @@ -14,26 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -119.0274, - 36.9559 - ], - [ - -88.1589, - 31.8534 - ], - [ - -149.6106, - 68.6307 - ], - [ - -84.2793, - 35.9574 - ], - [ - -105.9154, - 39.8914 - ], [ -102.4471, 39.7582 @@ -149,13 +129,33 @@ [ -111.5081, 33.751 + ], + [ + -119.0274, + 36.9559 + ], + [ + -88.1589, + 31.8534 + ], + [ + -149.6106, + 68.6307 + ], + [ + -84.2793, + 35.9574 + ], + [ + -105.9154, + 39.8914 ] ] }, "properties": { "title": "tg_randfor", - "description": "All forecasts for the Daily_Dissolved_oxygen variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_Dissolved_oxygen variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-18T00:00:00Z", "providers": [ @@ -186,11 +186,6 @@ "oxygen", "Daily", "P1D", - "TECR", - "TOMB", - "TOOK", - "WALK", - "WLOU", "ARIK", "BARC", "BIGC", @@ -219,7 +214,12 @@ "PRPO", "REDB", "SUGG", - "SYCA" + "SYCA", + "TECR", + "TOMB", + "TOOK", + "WALK", + "WLOU" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json index f7ca8ee45..cba2c2bf2 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json @@ -14,46 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -102.4471, - 39.7582 - ], - [ - -82.0084, - 29.676 - ], - [ - -119.2575, - 37.0597 - ], - [ - -110.5871, - 44.9501 - ], - [ - -96.6242, - 34.4442 - ], - [ - -87.7982, - 32.5415 - ], - [ - -147.504, - 65.1532 - ], - [ - -105.5442, - 40.035 - ], - [ - -89.4737, - 46.2097 - ], - [ - -66.9868, - 18.1135 - ], [ -84.4374, 31.1854 @@ -149,13 +109,53 @@ [ -105.9154, 39.8914 + ], + [ + -102.4471, + 39.7582 + ], + [ + -82.0084, + 29.676 + ], + [ + -119.2575, + 37.0597 + ], + [ + -110.5871, + 44.9501 + ], + [ + -96.6242, + 34.4442 + ], + [ + -87.7982, + 32.5415 + ], + [ + -147.504, + 65.1532 + ], + [ + -105.5442, + 40.035 + ], + [ + -89.4737, + 46.2097 + ], + [ + -66.9868, + 18.1135 ] ] }, "properties": { "title": "tg_tbats", - "description": "All forecasts for the Daily_Dissolved_oxygen variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_Dissolved_oxygen variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ @@ -186,16 +186,6 @@ "oxygen", "Daily", "P1D", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", - "BLWA", - "CARI", - "COMO", - "CRAM", - "CUPE", "FLNT", "GUIL", "HOPB", @@ -219,7 +209,17 @@ "TOMB", "TOOK", "WALK", - "WLOU" + "WLOU", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", + "BLWA", + "CARI", + "COMO", + "CRAM", + "CUPE" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm.json index 307823a18..85545bc09 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_temp_lm", "description": "All forecasts for the Daily_Dissolved_oxygen variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm_all_sites.json index b4b00c81b..6f06c0ee1 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm_all_sites.json @@ -155,13 +155,13 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All forecasts for the Daily_Dissolved_oxygen variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/collection.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/collection.json index 920b7efd4..86c7cce0a 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/collection.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/collection.json @@ -53,11 +53,6 @@ "type": "application/json", "href": "./models/tg_arima.json" }, - { - "rel": "item", - "type": "application/json", - "href": "./models/tg_ets.json" - }, { "rel": "item", "type": "application/json", @@ -81,12 +76,7 @@ { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/tg_ets.json" }, { "rel": "item", @@ -113,6 +103,16 @@ "type": "application/json", "href": "./models/tg_temp_lm_all_sites.json" }, + { + "rel": "item", + "type": "application/json", + "href": "./models/cb_prophet.json" + }, + { + "rel": "item", + "type": "application/json", + "href": "./models/climatology.json" + }, { "rel": "item", "type": "application/json", @@ -121,17 +121,17 @@ { "rel": "item", "type": "application/json", - "href": "./models/GLEON_JRabaey_temp_physics.json" + "href": "./models/baseline_ensemble.json" }, { "rel": "item", "type": "application/json", - "href": "./models/GLEON_lm_lag_1day.json" + "href": "./models/GLEON_JRabaey_temp_physics.json" }, { "rel": "item", "type": "application/json", - "href": "./models/GLEON_physics.json" + "href": "./models/GLEON_lm_lag_1day.json" }, { "rel": "item", @@ -141,7 +141,7 @@ { "rel": "item", "type": "application/json", - "href": "./models/baseline_ensemble.json" + "href": "./models/GLEON_physics.json" }, { "rel": "item", @@ -151,12 +151,12 @@ { "rel": "item", "type": "application/json", - "href": "./models/hotdeck.json" + "href": "./models/GAM_air_wind.json" }, { "rel": "item", "type": "application/json", - "href": "./models/zimmerman_proj1.json" + "href": "./models/TSLM_seasonal_JM.json" }, { "rel": "item", @@ -166,22 +166,22 @@ { "rel": "item", "type": "application/json", - "href": "./models/GAM_air_wind.json" + "href": "./models/hotdeck.json" }, { "rel": "item", "type": "application/json", - "href": "./models/TSLM_seasonal_JM.json" + "href": "./models/lm_AT_WTL_WS.json" }, { "rel": "item", "type": "application/json", - "href": "./models/lm_AT_WTL_WS.json" + "href": "./models/mlp1_wtempforecast_LF.json" }, { "rel": "item", "type": "application/json", - "href": "./models/mlp1_wtempforecast_LF.json" + "href": "./models/zimmerman_proj1.json" }, { "rel": "item", diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/GAM_air_wind.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/GAM_air_wind.json index 1f443bfd8..6bcc94577 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/GAM_air_wind.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/GAM_air_wind.json @@ -47,7 +47,7 @@ "properties": { "title": "GAM_air_wind", "description": "All forecasts for the Daily_Water_temperature variable for the GAM_air_wind model. Information for the model is provided as follows: I used a GAM (mgcv) with a linear relationship to air temperature and smoothing for eastward and northward winds..\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-01T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json index cd8a0452f..ab30a7058 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json @@ -155,7 +155,7 @@ "properties": { "title": "GLEON_JRabaey_temp_physics", "description": "All forecasts for the Daily_Water_temperature variable for the GLEON_JRabaey_temp_physics model. Information for the model is provided as follows: The JR-physics model is a simple process model based on the assumption that surface water\ntemperature should trend towards equilibration with air temperature with a lag factor..\n The model predicts this variable at the following sites: WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-12T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json index aa4e168f0..e250b2840 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json @@ -47,7 +47,7 @@ "properties": { "title": "GLEON_lm_lag_1day", "description": "All forecasts for the Daily_Water_temperature variable for the GLEON_lm_lag_1day model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-02-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/GLEON_physics.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/GLEON_physics.json index 452654c56..8974290f1 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/GLEON_physics.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/GLEON_physics.json @@ -43,7 +43,7 @@ "properties": { "title": "GLEON_physics", "description": "All forecasts for the Daily_Water_temperature variable for the GLEON_physics model. Information for the model is provided as follows: A simple, process-based model was developed to replicate the water temperature dynamics of a\nsurface water layer sensu Chapra (2008). The model focus was only on quantifying the impacts of\natmosphere-water heat flux exchanges on the idealized near-surface water temperature dynamics.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2023-12-22T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/TSLM_seasonal_JM.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/TSLM_seasonal_JM.json index 44b85778f..2e22488c7 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/TSLM_seasonal_JM.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/TSLM_seasonal_JM.json @@ -47,7 +47,7 @@ "properties": { "title": "TSLM_seasonal_JM", "description": "All forecasts for the Daily_Water_temperature variable for the TSLM_seasonal_JM model. Information for the model is provided as follows: My model uses the fable package TSLM, and uses built in exogenous regressors to represent the trend and seasonality of the data as well as air temperature to predict water temperature..\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-29T00:00:00Z", "end_datetime": "2024-06-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/acp_fableLM.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/acp_fableLM.json index 2a98f526e..3aad61c27 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/acp_fableLM.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/acp_fableLM.json @@ -47,7 +47,7 @@ "properties": { "title": "acp_fableLM", "description": "All forecasts for the Daily_Water_temperature variable for the acp_fableLM model. Information for the model is provided as follows: Time series linear model with FABLE.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-11T00:00:00Z", "end_datetime": "2024-04-13T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/air2waterSat_2.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/air2waterSat_2.json index 86cd5880b..802254507 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/air2waterSat_2.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/air2waterSat_2.json @@ -15,8 +15,16 @@ "type": "MultiPoint", "coordinates": [ [ - -77.9832, - 39.0956 + -149.6106, + 68.6307 + ], + [ + -84.2793, + 35.9574 + ], + [ + -105.9154, + 39.8914 ], [ -89.7048, @@ -139,29 +147,21 @@ 35.6904 ], [ - -149.6106, - 68.6307 - ], - [ - -84.2793, - 35.9574 - ], - [ - -105.9154, - 39.8914 + -77.9832, + 39.0956 ] ] }, "properties": { "title": "air2waterSat_2", - "description": "All forecasts for the Daily_Water_temperature variable for the air2waterSat_2 model. Information for the model is provided as follows: The air2water model is a linear model fit using the function lm() in R and uses air temperature as\na covariate.\n The model predicts this variable at the following sites: LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, TOOK, WALK, WLOU.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_Water_temperature variable for the air2waterSat_2 model. Information for the model is provided as follows: The air2water model is a linear model fit using the function lm() in R and uses air temperature as\na covariate.\n The model predicts this variable at the following sites: TOOK, WALK, WLOU, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ { "url": "https://github.com/rqthomas/neon4cast-example/blob/main/forecast_model.R", - "name": "Quinn Thomas", + "name": "Freya Olsson", "roles": [ "producer", "processor", @@ -186,7 +186,9 @@ "temperature", "Daily", "P1D", - "LEWI", + "TOOK", + "WALK", + "WLOU", "LIRO", "MART", "MAYF", @@ -217,9 +219,7 @@ "HOPB", "KING", "LECO", - "TOOK", - "WALK", - "WLOU" + "LEWI" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json index 1a256d20c..37aeb772f 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json @@ -14,14 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -96.443, - 38.9459 - ], - [ - -122.1655, - 44.2596 - ], [ -78.1473, 38.8943 @@ -38,6 +30,14 @@ -82.0177, 29.6878 ], + [ + -96.443, + 38.9459 + ], + [ + -122.1655, + 44.2596 + ], [ -72.3295, 42.4719 @@ -130,6 +130,10 @@ -89.7048, 45.9983 ], + [ + -147.504, + 65.1532 + ], [ -99.1139, 47.1591 @@ -138,10 +142,6 @@ -99.2531, 47.1298 ], - [ - -147.504, - 65.1532 - ], [ -149.143, 68.6698 @@ -154,8 +154,8 @@ }, "properties": { "title": "baseline_ensemble", - "description": "All forecasts for the Daily_Water_temperature variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: MCDI, MCRA, POSE, PRIN, REDB, SUGG, HOPB, KING, LECO, LEWI, MART, MAYF, SYCA, TECR, TOMB, WALK, WLOU, BLWA, COMO, CUPE, FLNT, GUIL, ARIK, BARC, BIGC, BLDE, BLUE, CRAM, LIRO, PRLA, PRPO, CARI, OKSR, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_Water_temperature variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: POSE, PRIN, REDB, SUGG, MCDI, MCRA, HOPB, KING, LECO, LEWI, MART, MAYF, SYCA, TECR, TOMB, WALK, WLOU, BLWA, COMO, CUPE, FLNT, GUIL, ARIK, BARC, BIGC, BLDE, BLUE, CRAM, LIRO, CARI, PRLA, PRPO, OKSR, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -186,12 +186,12 @@ "temperature", "Daily", "P1D", - "MCDI", - "MCRA", "POSE", "PRIN", "REDB", "SUGG", + "MCDI", + "MCRA", "HOPB", "KING", "LECO", @@ -215,9 +215,9 @@ "BLUE", "CRAM", "LIRO", + "CARI", "PRLA", "PRPO", - "CARI", "OKSR", "TOOK" ], diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/bee_bake_RFModel_2024.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/bee_bake_RFModel_2024.json index 813d5f562..5319bdcb4 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/bee_bake_RFModel_2024.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/bee_bake_RFModel_2024.json @@ -15,13 +15,21 @@ "type": "MultiPoint", "coordinates": [ [ - -89.4737, - 46.2097 + -89.7048, + 45.9983 + ], + [ + -99.2531, + 47.1298 ], [ -82.0084, 29.676 ], + [ + -89.4737, + 46.2097 + ], [ -99.1139, 47.1591 @@ -30,14 +38,6 @@ -82.0177, 29.6878 ], - [ - -99.2531, - 47.1298 - ], - [ - -89.7048, - 45.9983 - ], [ -149.6106, 68.6307 @@ -46,8 +46,8 @@ }, "properties": { "title": "bee_bake_RFModel_2024", - "description": "All forecasts for the Daily_Water_temperature variable for the bee_bake_RFModel_2024 model. Information for the model is provided as follows: Random Forest.\n The model predicts this variable at the following sites: CRAM, BARC, PRLA, SUGG, PRPO, LIRO, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_Water_temperature variable for the bee_bake_RFModel_2024 model. Information for the model is provided as follows: Random Forest.\n The model predicts this variable at the following sites: LIRO, PRPO, BARC, CRAM, PRLA, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-29T00:00:00Z", "end_datetime": "2024-09-24T00:00:00Z", "providers": [ @@ -78,12 +78,12 @@ "temperature", "Daily", "P1D", - "CRAM", + "LIRO", + "PRPO", "BARC", + "CRAM", "PRLA", "SUGG", - "PRPO", - "LIRO", "TOOK" ], "table:columns": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/cb_prophet.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/cb_prophet.json index 67859fda2..a7a39230a 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/cb_prophet.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/cb_prophet.json @@ -147,7 +147,7 @@ "properties": { "title": "cb_prophet", "description": "All forecasts for the Daily_Water_temperature variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, POSE, PRIN, PRLA, PRPO, REDB, SUGG, TECR, TOMB, WALK, WLOU, SYCA.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-10T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/climatology.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/climatology.json index 748cb15a3..56b69b4ad 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/climatology.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/climatology.json @@ -155,7 +155,7 @@ "properties": { "title": "climatology", "description": "All forecasts for the Daily_Water_temperature variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, WALK, WLOU, TOMB, LIRO, PRPO, CRAM, PRLA, CARI, OKSR, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-09-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json index 50dcd34a1..8edc1b899 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json @@ -155,7 +155,7 @@ "properties": { "title": "fARIMA_clim_ensemble", "description": "All forecasts for the Daily_Water_temperature variable for the fARIMA_clim_ensemble model. Information for the model is provided as follows: The fAMIRA-DOY MME is a multi-model ensemble (MME) composed of two empirical\nmodels: an ARIMA model (fARIMA) and day-of-year model.\n The model predicts this variable at the following sites: LECO, LEWI, MART, MAYF, MCDI, MCRA, COMO, CUPE, GUIL, HOPB, KING, ARIK, BARC, BLUE, BLWA, WALK, WLOU, POSE, PRIN, REDB, SUGG, TECR, TOMB, BIGC, BLDE, CRAM, FLNT, SYCA, LIRO, PRLA, PRPO, CARI, OKSR, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-10T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json index f02f551c7..4b585feca 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json @@ -155,7 +155,7 @@ "properties": { "title": "fTSLM_lag", "description": "All forecasts for the Daily_Water_temperature variable for the fTSLM_lag model. Information for the model is provided as follows: This is a simple time series linear model in which water temperature is a function of air\ntemperature of that day and the previous day\u2019s air temperature.\n The model predicts this variable at the following sites: TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-08T00:00:00Z", "end_datetime": "2024-09-14T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flareGLM.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flareGLM.json index 78b786bd5..40d8eb4db 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flareGLM.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flareGLM.json @@ -47,7 +47,7 @@ "properties": { "title": "flareGLM", "description": "All forecasts for the Daily_Water_temperature variable for the flareGLM model. Information for the model is provided as follows: The FLARE-GLM is a forecasting framework that integrates the General Lake Model\nhydrodynamic process model (GLM; Hipsey et al., 2019) and data assimilation algorithm to generate\nensemble forecasts of lake water temperature..\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json index 503b16852..1458dd4c2 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json @@ -47,13 +47,13 @@ "properties": { "title": "flareGLM_noDA", "description": "All forecasts for the Daily_Water_temperature variable for the flareGLM_noDA model. Information for the model is provided as follows: The FLARE-GLM is a forecasting framework that integrates the General Lake Model\nhydrodynamic process model (GLM; Hipsey et al., 2019). This version does not incorportate data assimilation.\n The model predicts this variable at the following sites: TOOK, BARC, CRAM, LIRO, PRLA, PRPO, SUGG.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-03-02T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ { "url": "https://github.com/FLARE-forecast/NEON-forecast-code/workflows/default", - "name": "Freya Olsson", + "name": "Joseph Rabaey", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flareGOTM_noDA.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flareGOTM_noDA.json index ef09a7213..0ca5af66e 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flareGOTM_noDA.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flareGOTM_noDA.json @@ -47,13 +47,13 @@ "properties": { "title": "flareGOTM_noDA", "description": "All forecasts for the Daily_Water_temperature variable for the flareGOTM_noDA model. Information for the model is provided as follows: FLARE-GOTM uses the General Ocean Turbulence Model (GOTM) hydrodynamic model. GOTM is a 1-D\nhydrodynamic turbulence model (Umlauf et al., 2005) that estimates water column temperatures.\n The model predicts this variable at the following sites: BARC, CRAM, SUGG, LIRO, PRLA, PRPO, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-03-08T00:00:00Z", "end_datetime": "2024-03-20T00:00:00Z", "providers": [ { "url": "https://github.com/FLARE-forecast/NEON-forecast-code/workflows/ler", - "name": "Joseph Rabaey", + "name": "Quinn Thomas", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flareSimstrat_noDA.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flareSimstrat_noDA.json index 5f135d440..6824c7da8 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flareSimstrat_noDA.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flareSimstrat_noDA.json @@ -43,13 +43,13 @@ "properties": { "title": "flareSimstrat_noDA", "description": "All forecasts for the Daily_Water_temperature variable for the flareSimstrat_noDA model. Information for the model is provided as follows: FLARE-Simstrat uses the same principles and overarching framework as FLARE-GLM with the\nhydrodynamic model replaced with Simstrat. Simstrat is a 1-D hydrodynamic turbulence model\n(Goudsmit et al., 2002) that estimates water column temperatures..\n The model predicts this variable at the following sites: BARC, SUGG, TOOK, CRAM, PRLA, PRPO.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-03-08T00:00:00Z", "end_datetime": "2024-03-19T00:00:00Z", "providers": [ { - "url": "https://github.com/FLARE-forecast/NEON-forecast-code/workflows/ler", - "name": "Quinn Thomas", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flare_ler.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flare_ler.json index 5b07eaf2b..c10d229ca 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flare_ler.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flare_ler.json @@ -43,7 +43,7 @@ "properties": { "title": "flare_ler", "description": "All forecasts for the Daily_Water_temperature variable for the flare_ler model. Information for the model is provided as follows: The LER MME is a multi-model ensemble (MME) derived from the three process models from\nFLARE (FLARE-GLM, FLARE-GOTM, and FLARE-Simstrat). To generate the MME, an ensemble\nforecast was generated by sampling from the submitted models\u2019 ensemble members.\n The model predicts this variable at the following sites: SUGG, CRAM, LIRO, PRLA, PRPO, BARC.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-19T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flare_ler_baselines.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flare_ler_baselines.json index c3a9df661..fe905fd25 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flare_ler_baselines.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/flare_ler_baselines.json @@ -27,7 +27,7 @@ "properties": { "title": "flare_ler_baselines", "description": "All forecasts for the Daily_Water_temperature variable for the flare_ler_baselines model. Information for the model is provided as follows: The LER-baselines model is a multi-model ensemble (MME) comprised of the three process\nmodels from FLARE (FLARE-GLM, FLARE-GOTM, and FLARE-Simstrat) and the two baseline\nmodels (day-of-year, persistence), submitted by Challenge organisers. To generate the MME, an\nensemble forecast was generated by sampling from the submitted model\u2019s ensemble members (either\nfrom an ensemble forecast in the case of the FLARE models and persistence, or from the distribution for\nthe day-of-year forecasts).\n The model predicts this variable at the following sites: SUGG, BARC.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-19T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/hotdeck.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/hotdeck.json index f186bd199..246c54a6d 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/hotdeck.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/hotdeck.json @@ -139,7 +139,7 @@ "properties": { "title": "hotdeck", "description": "All forecasts for the Daily_Water_temperature variable for the hotdeck model. Information for the model is provided as follows: Uses a hot deck approach: - Take the latest observation/forecast. - Past observations from around the same window of the season are collected. - Values close to the latest observation/forecast are collected. - One of these is randomly sampled. - Its \"tomorrow\" observation is used as the forecast. - Repeat until forecast at step h..\n The model predicts this variable at the following sites: BARC, SUGG, TOMB, BLWA, FLNT, MCRA, KING, SYCA, POSE, PRIN, MAYF, LEWI, LECO, ARIK, HOPB, REDB, TECR, BLDE, COMO, WLOU, CRAM, CARI, BIGC, BLUE, CUPE, GUIL, WALK, LIRO, PRLA, PRPO.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-28T00:00:00Z", "end_datetime": "2024-09-21T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/lm_AT_WTL_WS.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/lm_AT_WTL_WS.json index 2a7f58865..baef4bdab 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/lm_AT_WTL_WS.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/lm_AT_WTL_WS.json @@ -47,7 +47,7 @@ "properties": { "title": "lm_AT_WTL_WS", "description": "All forecasts for the Daily_Water_temperature variable for the lm_AT_WTL_WS model. Information for the model is provided as follows: This forecast of water temperature at NEON Lake sites uses a linear model, incorporating air temperature, wind speed, and the previous day's forecasted water temperature as variables..\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-01T00:00:00Z", "end_datetime": "2024-09-21T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/mkricheldorf_w_lag.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/mkricheldorf_w_lag.json index d7b11d1df..b5208512d 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/mkricheldorf_w_lag.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/mkricheldorf_w_lag.json @@ -47,7 +47,7 @@ "properties": { "title": "mkricheldorf_w_lag", "description": "All forecasts for the Daily_Water_temperature variable for the mkricheldorf_w_lag model. Information for the model is provided as follows: I used an autoregressive linear model using the lm() function.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-06T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/mlp1_wtempforecast_LF.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/mlp1_wtempforecast_LF.json index 7f24e3922..ec7a99277 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/mlp1_wtempforecast_LF.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/mlp1_wtempforecast_LF.json @@ -47,7 +47,7 @@ "properties": { "title": "mlp1_wtempforecast_LF", "description": "All forecasts for the Daily_Water_temperature variable for the mlp1_wtempforecast_LF model. Information for the model is provided as follows: Modelling for water temperature using a single layer neural network (mlp() in tidymodels). Used relative humidity, precipitation flux and air temperature as drivers. Hypertuned parameters for models to be run with 100 epochs and penalty value of 0.01..\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-01T00:00:00Z", "end_datetime": "2024-09-24T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/persistenceRW.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/persistenceRW.json index 07687f7b8..ec6a9b138 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/persistenceRW.json @@ -155,7 +155,7 @@ "properties": { "title": "persistenceRW", "description": "All forecasts for the Daily_Water_temperature variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: KING, LECO, LEWI, LIRO, MART, MAYF, ARIK, BARC, BIGC, BLDE, BLUE, MCDI, MCRA, OKSR, POSE, PRIN, WLOU, CUPE, FLNT, GUIL, HOPB, PRLA, PRPO, REDB, SUGG, SYCA, BLWA, CARI, COMO, CRAM, TECR, TOMB, TOOK, WALK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/precip_mod.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/precip_mod.json index c2e3200cb..4d89a48a0 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/precip_mod.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/precip_mod.json @@ -47,7 +47,7 @@ "properties": { "title": "precip_mod", "description": "All forecasts for the Daily_Water_temperature variable for the precip_mod model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-12-21T00:00:00Z", "end_datetime": "2024-01-24T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_arima.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_arima.json index 078800587..69c1e97d1 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_arima.json @@ -155,13 +155,13 @@ "properties": { "title": "tg_arima", "description": "All forecasts for the Daily_Water_temperature variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Gregory Harrison", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_ets.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_ets.json index 30b054e42..ca79b54e2 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_ets.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_ets", "description": "All forecasts for the Daily_Water_temperature variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_humidity_lm.json index 50817ee32..faa97739d 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_humidity_lm.json @@ -155,13 +155,13 @@ "properties": { "title": "tg_humidity_lm", "description": "All forecasts for the Daily_Water_temperature variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ { - "url": "https://projects.ecoforecast.org/neon4cast-ci/", - "name": "NEON Ecological Forecasting Project", + "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", + "name": "Abigail Lewis", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_humidity_lm_all_sites.json index cf628c943..71e4db30d 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_humidity_lm_all_sites.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All forecasts for the Daily_Water_temperature variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_lasso.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_lasso.json index 0aa1982bc..935637f5c 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_lasso.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_lasso.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_lasso", "description": "All forecasts for the Daily_Water_temperature variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_precip_lm.json index a0abb4a65..d126a07e0 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_precip_lm.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_precip_lm", "description": "All forecasts for the Daily_Water_temperature variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_precip_lm_all_sites.json index a7512b54a..97aa23aa5 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_precip_lm_all_sites.json @@ -155,13 +155,13 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All forecasts for the Daily_Water_temperature variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-19T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "name": "Gregory Harrison", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_randfor.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_randfor.json index 6a851793a..81776bc47 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_randfor.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_randfor", "description": "All forecasts for the Daily_Water_temperature variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_tbats.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_tbats.json index 7aea52c21..73245530a 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_tbats.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_tbats", "description": "All forecasts for the Daily_Water_temperature variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_temp_lm.json index 44ceb268f..39de59997 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_temp_lm.json @@ -14,6 +14,18 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -149.6106, + 68.6307 + ], + [ + -84.2793, + 35.9574 + ], + [ + -105.9154, + 39.8914 + ], [ -102.4471, 39.7582 @@ -137,25 +149,13 @@ [ -88.1589, 31.8534 - ], - [ - -149.6106, - 68.6307 - ], - [ - -84.2793, - 35.9574 - ], - [ - -105.9154, - 39.8914 ] ] }, "properties": { "title": "tg_temp_lm", - "description": "All forecasts for the Daily_Water_temperature variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_Water_temperature variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -186,6 +186,9 @@ "temperature", "Daily", "P1D", + "TOOK", + "WALK", + "WLOU", "ARIK", "BARC", "BIGC", @@ -216,10 +219,7 @@ "SUGG", "SYCA", "TECR", - "TOMB", - "TOOK", - "WALK", - "WLOU" + "TOMB" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_temp_lm_all_sites.json index f0e219382..a719470ba 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/tg_temp_lm_all_sites.json @@ -155,13 +155,13 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All forecasts for the Daily_Water_temperature variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/zimmerman_proj1.json b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/zimmerman_proj1.json index 57d94dea3..9bf19f973 100644 --- a/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/zimmerman_proj1.json +++ b/data/challenge/neon4cast-stac/forecasts/Aquatics/Daily_Water_temperature/models/zimmerman_proj1.json @@ -47,7 +47,7 @@ "properties": { "title": "zimmerman_proj1", "description": "All forecasts for the Daily_Water_temperature variable for the zimmerman_proj1 model. Information for the model is provided as follows: I used an ARIMA model with one autoregressive term. I also included air pressure and air temperature.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-28T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/collection.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/collection.json index 0ea98ca8f..05e553441 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/collection.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/collection.json @@ -8,11 +8,6 @@ ], "type": "Collection", "links": [ - { - "rel": "item", - "type": "application/json", - "href": "./models/tg_arima.json" - }, { "rel": "item", "type": "application/json", @@ -51,18 +46,23 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", "href": "./models/tg_precip_lm_all_sites.json" }, + { + "rel": "item", + "type": "application/json", + "href": "./models/tg_temp_lm.json" + }, { "rel": "parent", "type": "application/json", diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json index 71ba4c045..19ebcbf81 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_arima", "description": "All forecasts for the Weekly_beetle_community_abundance variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-02-13T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Gregory Harrison", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json index 8c2bfe1f5..01ea3aa70 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json @@ -14,62 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], [ -149.2133, 63.8758 @@ -201,13 +145,69 @@ [ -110.5391, 44.9535 + ], + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 ] ] }, "properties": { "title": "tg_ets", - "description": "All forecasts for the Weekly_beetle_community_abundance variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Weekly_beetle_community_abundance variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-02-06T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ @@ -238,20 +238,6 @@ "abundance", "Weekly", "P1W", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", "HEAL", "JERC", "JORN", @@ -284,7 +270,21 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm.json index 43cc0ad24..ffd180b3e 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_humidity_lm", "description": "All forecasts for the Weekly_beetle_community_abundance variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ { - "url": "https://projects.ecoforecast.org/neon4cast-ci/", - "name": "NEON Ecological Forecasting Project", + "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", + "name": "Abigail Lewis", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm_all_sites.json index 5f07e25e8..4f0b48443 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All forecasts for the Weekly_beetle_community_abundance variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_lasso.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_lasso.json index d55feeda8..dc997e9ce 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_lasso.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_lasso.json @@ -199,7 +199,7 @@ "properties": { "title": "tg_lasso", "description": "All forecasts for the Weekly_beetle_community_abundance variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: ABBY, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm.json index 548c1d5e7..0579a1361 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_precip_lm", "description": "All forecasts for the Weekly_beetle_community_abundance variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm_all_sites.json index f4c70a0a5..12a76a21a 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm_all_sites.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All forecasts for the Weekly_beetle_community_abundance variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "name": "Gregory Harrison", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_randfor.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_randfor.json index 4b4f62c95..2ced329ad 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_randfor.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_randfor", "description": "All forecasts for the Weekly_beetle_community_abundance variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-19T00:00:00Z", "end_datetime": "2024-03-01T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json index 8abc6a307..10d67e4f8 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_tbats", "description": "All forecasts for the Weekly_beetle_community_abundance variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm.json index 10feae363..5b5b34272 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_temp_lm", "description": "All forecasts for the Weekly_beetle_community_abundance variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm_all_sites.json index 9e03e54a1..201656126 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm_all_sites.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All forecasts for the Weekly_beetle_community_abundance variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/collection.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/collection.json index dbcde56fc..a614eaa78 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/collection.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/collection.json @@ -11,42 +11,42 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_lasso.json" + "href": "./models/tg_ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_lasso.json" }, { "rel": "item", diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_arima.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_arima.json index 5e6b582b5..9d12f3afd 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_arima.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_arima", "description": "All forecasts for the Weekly_beetle_community_richness variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-02-13T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Gregory Harrison", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_ets.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_ets.json index 5f6c5e957..0cef13122 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_ets.json @@ -14,82 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -81.9934, - 29.6893 - ], - [ - -155.3173, - 19.5531 - ], - [ - -105.546, - 40.2759 - ], - [ - -78.1395, - 38.8929 - ], - [ - -76.56, - 38.8901 - ], - [ - -119.7323, - 37.1088 - ], - [ - -119.2622, - 37.0334 - ], - [ - -110.8355, - 31.9107 - ], - [ - -89.5864, - 45.5089 - ], - [ - -103.0293, - 40.4619 - ], - [ - -87.3933, - 32.9505 - ], - [ - -119.006, - 37.0058 - ], - [ - -149.3705, - 68.6611 - ], - [ - -89.5857, - 45.4937 - ], - [ - -95.1921, - 39.0404 - ], - [ - -89.5373, - 46.2339 - ], - [ - -99.2413, - 47.1282 - ], - [ - -121.9519, - 45.8205 - ], - [ - -110.5391, - 44.9535 - ], [ -122.3303, 45.7624 @@ -201,13 +125,89 @@ [ -84.2826, 35.9641 + ], + [ + -81.9934, + 29.6893 + ], + [ + -155.3173, + 19.5531 + ], + [ + -105.546, + 40.2759 + ], + [ + -78.1395, + 38.8929 + ], + [ + -76.56, + 38.8901 + ], + [ + -119.7323, + 37.1088 + ], + [ + -119.2622, + 37.0334 + ], + [ + -110.8355, + 31.9107 + ], + [ + -89.5864, + 45.5089 + ], + [ + -103.0293, + 40.4619 + ], + [ + -87.3933, + 32.9505 + ], + [ + -119.006, + 37.0058 + ], + [ + -149.3705, + 68.6611 + ], + [ + -89.5857, + 45.4937 + ], + [ + -95.1921, + 39.0404 + ], + [ + -89.5373, + 46.2339 + ], + [ + -99.2413, + 47.1282 + ], + [ + -121.9519, + 45.8205 + ], + [ + -110.5391, + 44.9535 ] ] }, "properties": { "title": "tg_ets", - "description": "All forecasts for the Weekly_beetle_community_richness variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Weekly_beetle_community_richness variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-02-06T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ @@ -238,25 +238,6 @@ "richness", "Weekly", "P1W", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -284,7 +265,26 @@ "NOGP", "OAES", "ONAQ", - "ORNL" + "ORNL", + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm.json index 892cf3c21..0bf7305c9 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_humidity_lm", "description": "All forecasts for the Weekly_beetle_community_richness variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ { - "url": "https://projects.ecoforecast.org/neon4cast-ci/", - "name": "NEON Ecological Forecasting Project", + "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", + "name": "Abigail Lewis", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm_all_sites.json index b46ddbe0f..0109f027a 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All forecasts for the Weekly_beetle_community_richness variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_lasso.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_lasso.json index 92cbc025f..a859f7bb4 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_lasso.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_lasso.json @@ -199,7 +199,7 @@ "properties": { "title": "tg_lasso", "description": "All forecasts for the Weekly_beetle_community_richness variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: ABBY, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm.json index 5596b7ca0..74c1158e4 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_precip_lm", "description": "All forecasts for the Weekly_beetle_community_richness variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm_all_sites.json index 21d8214b3..c2de34661 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm_all_sites.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All forecasts for the Weekly_beetle_community_richness variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "name": "Gregory Harrison", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_randfor.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_randfor.json index 54ee7370b..b1d3cf24a 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_randfor.json @@ -14,70 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 - ], - [ - -84.4686, - 31.1948 - ], [ -106.8425, 32.5907 @@ -201,13 +137,77 @@ [ -110.5391, 44.9535 + ], + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 + ], + [ + -84.4686, + 31.1948 ] ] }, "properties": { "title": "tg_randfor", - "description": "All forecasts for the Weekly_beetle_community_richness variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Weekly_beetle_community_richness variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-19T00:00:00Z", "end_datetime": "2024-03-01T00:00:00Z", "providers": [ @@ -238,22 +238,6 @@ "richness", "Weekly", "P1W", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", "JORN", "KONA", "KONZ", @@ -284,7 +268,23 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json index f504c8b79..f233f80f4 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_tbats", "description": "All forecasts for the Weekly_beetle_community_richness variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm.json index cb6a33481..1b272ec29 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_temp_lm", "description": "All forecasts for the Weekly_beetle_community_richness variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm_all_sites.json index fdfbea968..e12dd3b6f 100644 --- a/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm_all_sites.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All forecasts for the Weekly_beetle_community_richness variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/collection.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/collection.json index 820f16f45..27df9df31 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/collection.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/collection.json @@ -11,72 +11,72 @@ { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/tg_lasso.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/tg_temp_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_lasso.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/tg_ets.json" }, { "rel": "item", diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json index 0760cf693..4547cd564 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json @@ -27,7 +27,7 @@ "properties": { "title": "ChlorophyllCrusaders", "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the ChlorophyllCrusaders model. Information for the model is provided as follows: Our project utilizes a historical GCC data to fit a Dynamic Linear Model (DLM). After this DLM is trained, we utilize forecasted temperature data to predict future GCC data..\n The model predicts this variable at the following sites: HARV, HEAL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", "end_datetime": "2024-06-20T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/PEG.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/PEG.json index 28098178f..c74e5d8d8 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/PEG.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/PEG.json @@ -207,7 +207,7 @@ "properties": { "title": "PEG", "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the PEG model. Information for the model is provided as follows: This model was a Simple Seasonal + Exponential Smoothing Model, with the GCC targets as inputs.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-12-22T00:00:00Z", "end_datetime": "2024-01-25T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/cb_prophet.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/cb_prophet.json index baac2376f..05bee0329 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/cb_prophet.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/cb_prophet.json @@ -14,6 +14,66 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -76.56, + 38.8901 + ], + [ + -119.7323, + 37.1088 + ], + [ + -119.2622, + 37.0334 + ], + [ + -110.8355, + 31.9107 + ], + [ + -89.5864, + 45.5089 + ], + [ + -103.0293, + 40.4619 + ], + [ + -87.3933, + 32.9505 + ], + [ + -119.006, + 37.0058 + ], + [ + -149.3705, + 68.6611 + ], + [ + -89.5857, + 45.4937 + ], + [ + -95.1921, + 39.0404 + ], + [ + -89.5373, + 46.2339 + ], + [ + -99.2413, + 47.1282 + ], + [ + -121.9519, + 45.8205 + ], + [ + -110.5391, + 44.9535 + ], [ -122.3303, 45.7624 @@ -141,73 +201,13 @@ [ -78.1395, 38.8929 - ], - [ - -76.56, - 38.8901 - ], - [ - -119.7323, - 37.1088 - ], - [ - -119.2622, - 37.0334 - ], - [ - -110.8355, - 31.9107 - ], - [ - -89.5864, - 45.5089 - ], - [ - -103.0293, - 40.4619 - ], - [ - -87.3933, - 32.9505 - ], - [ - -119.006, - 37.0058 - ], - [ - -149.3705, - 68.6611 - ], - [ - -89.5857, - 45.4937 - ], - [ - -95.1921, - 39.0404 - ], - [ - -89.5373, - 46.2339 - ], - [ - -99.2413, - 47.1282 - ], - [ - -121.9519, - 45.8205 - ], - [ - -110.5391, - 44.9535 ] ] }, "properties": { "title": "cb_prophet", - "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-09T00:00:00Z", "providers": [ @@ -238,6 +238,21 @@ "gcc_90", "Daily", "P1D", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -269,22 +284,7 @@ "OSBS", "PUUM", "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "SCBI" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json index 0b49a4da5..5d1a74874 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json @@ -207,7 +207,7 @@ "properties": { "title": "climatology", "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, BONA, DEJU, HEAL, TOOL, BARR.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-07T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json index c47ff6374..f098f54f2 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json @@ -59,104 +59,116 @@ 63.8811 ], [ - -81.9934, - 29.6893 + -119.7323, + 37.1088 ], [ - -155.3173, - 19.5531 + -119.2622, + 37.0334 ], [ - -105.546, - 40.2759 + -110.8355, + 31.9107 ], [ - -78.1395, - 38.8929 + -89.5864, + 45.5089 ], [ - -76.56, - 38.8901 + -103.0293, + 40.4619 ], [ - -119.7323, - 37.1088 + -87.3933, + 32.9505 ], [ - -67.0769, - 18.0213 + -100.9154, + 46.7697 ], [ - -88.1612, - 31.8539 + -99.0588, + 35.4106 ], [ - -80.5248, - 37.3783 + -112.4524, + 40.1776 ], [ - -109.3883, - 38.2483 + -84.2826, + 35.9641 ], [ - -105.5824, - 40.0543 + -81.9934, + 29.6893 ], [ - -89.5373, - 46.2339 + -119.006, + 37.0058 ], [ - -99.2413, - 47.1282 + -149.3705, + 68.6611 ], [ - -121.9519, - 45.8205 + -89.5857, + 45.4937 ], [ - -110.5391, - 44.9535 + -95.1921, + 39.0404 ], [ - -100.9154, - 46.7697 + -89.5373, + 46.2339 ], [ - -119.2622, - 37.0334 + -67.0769, + 18.0213 ], [ - -110.8355, - 31.9107 + -88.1612, + 31.8539 ], [ - -89.5864, - 45.5089 + -80.5248, + 37.3783 ], [ - -103.0293, - 40.4619 + -109.3883, + 38.2483 ], [ - -87.3933, - 32.9505 + -105.5824, + 40.0543 ], [ - -119.006, - 37.0058 + -155.3173, + 19.5531 ], [ - -149.3705, - 68.6611 + -105.546, + 40.2759 ], [ - -89.5857, - 45.4937 + -78.1395, + 38.8929 ], [ - -95.1921, - 39.0404 + -76.56, + 38.8901 + ], + [ + -99.2413, + 47.1282 + ], + [ + -121.9519, + 45.8205 + ], + [ + -110.5391, + 44.9535 ], [ -122.3303, @@ -174,18 +186,6 @@ -78.0418, 39.0337 ], - [ - -99.0588, - 35.4106 - ], - [ - -112.4524, - 40.1776 - ], - [ - -84.2826, - 35.9641 - ], [ -84.4686, 31.1948 @@ -206,8 +206,8 @@ }, "properties": { "title": "persistenceRW", - "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: DELA, DSNY, GRSM, GUAN, HARV, HEAL, BONA, CLBJ, CPER, DCFS, DEJU, OSBS, PUUM, RMNP, SCBI, SERC, SJER, LAJA, LENO, MLBS, MOAB, NIWO, UNDE, WOOD, WREF, YELL, NOGP, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, ABBY, BARR, BART, BLAN, OAES, ONAQ, ORNL, JERC, JORN, KONA, KONZ.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: DELA, DSNY, GRSM, GUAN, HARV, HEAL, BONA, CLBJ, CPER, DCFS, DEJU, SJER, SOAP, SRER, STEI, STER, TALL, NOGP, OAES, ONAQ, ORNL, OSBS, TEAK, TOOL, TREE, UKFS, UNDE, LAJA, LENO, MLBS, MOAB, NIWO, PUUM, RMNP, SCBI, SERC, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, JERC, JORN, KONA, KONZ.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ @@ -249,38 +249,38 @@ "CPER", "DCFS", "DEJU", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", "SJER", - "LAJA", - "LENO", - "MLBS", - "MOAB", - "NIWO", - "UNDE", - "WOOD", - "WREF", - "YELL", - "NOGP", "SOAP", "SRER", "STEI", "STER", "TALL", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", "TEAK", "TOOL", "TREE", "UKFS", + "UNDE", + "LAJA", + "LENO", + "MLBS", + "MOAB", + "NIWO", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", "BLAN", - "OAES", - "ONAQ", - "ORNL", "JERC", "JORN", "KONA", diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json index e5c81eaea..ceb0069be 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json @@ -14,6 +14,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -89.5373, + 46.2339 + ], + [ + -99.2413, + 47.1282 + ], + [ + -121.9519, + 45.8205 + ], + [ + -110.5391, + 44.9535 + ], [ -122.3303, 45.7624 @@ -185,35 +201,19 @@ [ -95.1921, 39.0404 - ], - [ - -89.5373, - 46.2339 - ], - [ - -99.2413, - 47.1282 - ], - [ - -121.9519, - 45.8205 - ], - [ - -110.5391, - 44.9535 ] ] }, "properties": { "title": "tg_arima", - "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Gregory Harrison", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", @@ -238,6 +238,10 @@ "gcc_90", "Daily", "P1D", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -280,11 +284,7 @@ "TEAK", "TOOL", "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "UKFS" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json index 7be2ab8e9..db972cb40 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_ets", "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm.json index 994da41c7..df0f2e2db 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm.json @@ -14,6 +14,74 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -149.2133, + 63.8758 + ], + [ + -84.4686, + 31.1948 + ], + [ + -106.8425, + 32.5907 + ], + [ + -96.6129, + 39.1104 + ], + [ + -96.5631, + 39.1008 + ], + [ + -67.0769, + 18.0213 + ], + [ + -88.1612, + 31.8539 + ], + [ + -80.5248, + 37.3783 + ], + [ + -109.3883, + 38.2483 + ], + [ + -105.5824, + 40.0543 + ], + [ + -100.9154, + 46.7697 + ], + [ + -99.0588, + 35.4106 + ], + [ + -112.4524, + 40.1776 + ], + [ + -84.2826, + 35.9641 + ], + [ + -81.9934, + 29.6893 + ], + [ + -155.3173, + 19.5531 + ], + [ + -105.546, + 40.2759 + ], [ -78.1395, 38.8929 @@ -114,74 +182,6 @@ -145.7514, 63.8811 ], - [ - -149.2133, - 63.8758 - ], - [ - -84.4686, - 31.1948 - ], - [ - -106.8425, - 32.5907 - ], - [ - -96.6129, - 39.1104 - ], - [ - -96.5631, - 39.1008 - ], - [ - -67.0769, - 18.0213 - ], - [ - -88.1612, - 31.8539 - ], - [ - -80.5248, - 37.3783 - ], - [ - -109.3883, - 38.2483 - ], - [ - -105.5824, - 40.0543 - ], - [ - -100.9154, - 46.7697 - ], - [ - -99.0588, - 35.4106 - ], - [ - -112.4524, - 40.1776 - ], - [ - -84.2826, - 35.9641 - ], - [ - -81.9934, - 29.6893 - ], - [ - -155.3173, - 19.5531 - ], - [ - -105.546, - 40.2759 - ], [ -87.8039, 32.5417 @@ -206,14 +206,14 @@ }, "properties": { "title": "tg_humidity_lm", - "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, DELA, DSNY, GRSM, GUAN, HARV.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ { - "url": "https://projects.ecoforecast.org/neon4cast-ci/", - "name": "NEON Ecological Forecasting Project", + "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", + "name": "Abigail Lewis", "roles": [ "producer", "processor", @@ -238,6 +238,23 @@ "gcc_90", "Daily", "P1D", + "HEAL", + "JERC", + "JORN", + "KONA", + "KONZ", + "LAJA", + "LENO", + "MLBS", + "MOAB", + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP", "SCBI", "SERC", "SJER", @@ -263,23 +280,6 @@ "CPER", "DCFS", "DEJU", - "HEAL", - "JERC", - "JORN", - "KONA", - "KONZ", - "LAJA", - "LENO", - "MLBS", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", "DELA", "DSNY", "GRSM", diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm_all_sites.json index 665de0fb2..30fa5992f 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_lasso.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_lasso.json index abd32239e..35c2a4629 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_lasso.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_lasso.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_lasso", "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm.json index bae7eda45..bcda8df8d 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_precip_lm", "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm_all_sites.json index 5cf5d3f44..6b1adfd22 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm_all_sites.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "name": "Gregory Harrison", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_randfor.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_randfor.json index b37284143..88429c80b 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_randfor.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_randfor", "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json index d547cb644..04af50bc0 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_tbats", "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm.json index 207b66228..6a6fc1fbc 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_temp_lm", "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm_all_sites.json index b6f968cf0..b263a282d 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm_all_sites.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/collection.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/collection.json index 6a3873b75..ff66eaf44 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/collection.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/collection.json @@ -11,62 +11,62 @@ { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/baseline_ensemble.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/baseline_ensemble.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/tg_ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_lasso.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/tg_lasso.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/PEG.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/PEG.json index b4c617b32..67ad557ac 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/PEG.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/PEG.json @@ -207,7 +207,7 @@ "properties": { "title": "PEG", "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the PEG model. Information for the model is provided as follows: This model was a Simple Seasonal + Exponential Smoothing Model, with the GCC targets as inputs.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-12-22T00:00:00Z", "end_datetime": "2024-01-25T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json index bb8e0f114..fb878e71f 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json @@ -182,10 +182,6 @@ -100.9154, 46.7697 ], - [ - -149.3705, - 68.6611 - ], [ -147.5026, 65.154 @@ -201,13 +197,17 @@ [ -156.6194, 71.2824 + ], + [ + -149.3705, + 68.6611 ] ] }, "properties": { "title": "baseline_ensemble", - "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, SERC, SJER, SOAP, SRER, STEI, UNDE, WOOD, WREF, YELL, STER, TALL, TEAK, TREE, UKFS, DELA, DSNY, GRSM, GUAN, MLBS, MOAB, NIWO, NOGP, TOOL, BONA, DEJU, HEAL, BARR.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, SERC, SJER, SOAP, SRER, STEI, UNDE, WOOD, WREF, YELL, STER, TALL, TEAK, TREE, UKFS, DELA, DSNY, GRSM, GUAN, MLBS, MOAB, NIWO, NOGP, BONA, DEJU, HEAL, BARR, TOOL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -280,11 +280,11 @@ "MOAB", "NIWO", "NOGP", - "TOOL", "BONA", "DEJU", "HEAL", - "BARR" + "BARR", + "TOOL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/cb_prophet.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/cb_prophet.json index 2f5bf37bd..8fcb40b63 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/cb_prophet.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/cb_prophet.json @@ -207,7 +207,7 @@ "properties": { "title": "cb_prophet", "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-09T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json index 2ebe18c7a..73aa564ef 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json @@ -207,7 +207,7 @@ "properties": { "title": "climatology", "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, DEJU, HEAL, BONA, TOOL, BARR.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-07T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json index 35dd49d06..38a7a5d41 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json @@ -207,7 +207,7 @@ "properties": { "title": "persistenceRW", "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: KONA, KONZ, LAJA, LENO, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, ONAQ, ORNL, OSBS, PUUM, SRER, STEI, STER, TALL, TEAK, RMNP, SCBI, SERC, SJER, SOAP, OAES, JERC, JORN, MLBS, MOAB, NIWO, NOGP, TOOL, TREE, UKFS, UNDE, GRSM, GUAN, HARV, HEAL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json index 33424d65a..8a613f30d 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json @@ -14,38 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], [ -87.8039, 32.5417 @@ -201,19 +169,51 @@ [ -122.3303, 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 ] ] }, "properties": { "title": "tg_arima", - "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Gregory Harrison", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", @@ -238,14 +238,6 @@ "rcc_90", "Daily", "P1D", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", "DELA", "DSNY", "GRSM", @@ -284,7 +276,15 @@ "WOOD", "WREF", "YELL", - "ABBY" + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json index 76e4c03b9..8e33ca712 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json @@ -14,86 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -84.2826, - 35.9641 - ], - [ - -81.9934, - 29.6893 - ], - [ - -155.3173, - 19.5531 - ], - [ - -105.546, - 40.2759 - ], - [ - -78.1395, - 38.8929 - ], - [ - -76.56, - 38.8901 - ], - [ - -119.7323, - 37.1088 - ], - [ - -119.2622, - 37.0334 - ], - [ - -110.8355, - 31.9107 - ], - [ - -89.5864, - 45.5089 - ], - [ - -103.0293, - 40.4619 - ], - [ - -87.3933, - 32.9505 - ], - [ - -119.006, - 37.0058 - ], - [ - -149.3705, - 68.6611 - ], - [ - -89.5857, - 45.4937 - ], - [ - -95.1921, - 39.0404 - ], - [ - -89.5373, - 46.2339 - ], - [ - -99.2413, - 47.1282 - ], - [ - -121.9519, - 45.8205 - ], - [ - -110.5391, - 44.9535 - ], [ -122.3303, 45.7624 @@ -201,13 +121,93 @@ [ -112.4524, 40.1776 + ], + [ + -84.2826, + 35.9641 + ], + [ + -81.9934, + 29.6893 + ], + [ + -155.3173, + 19.5531 + ], + [ + -105.546, + 40.2759 + ], + [ + -78.1395, + 38.8929 + ], + [ + -76.56, + 38.8901 + ], + [ + -119.7323, + 37.1088 + ], + [ + -119.2622, + 37.0334 + ], + [ + -110.8355, + 31.9107 + ], + [ + -89.5864, + 45.5089 + ], + [ + -103.0293, + 40.4619 + ], + [ + -87.3933, + 32.9505 + ], + [ + -119.006, + 37.0058 + ], + [ + -149.3705, + 68.6611 + ], + [ + -89.5857, + 45.4937 + ], + [ + -95.1921, + 39.0404 + ], + [ + -89.5373, + 46.2339 + ], + [ + -99.2413, + 47.1282 + ], + [ + -121.9519, + 45.8205 + ], + [ + -110.5391, + 44.9535 ] ] }, "properties": { "title": "tg_ets", - "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -238,26 +238,6 @@ "rcc_90", "Daily", "P1D", - "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -284,7 +264,27 @@ "NIWO", "NOGP", "OAES", - "ONAQ" + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm.json index e80994423..dbed35d7c 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_humidity_lm", "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ { - "url": "https://projects.ecoforecast.org/neon4cast-ci/", - "name": "NEON Ecological Forecasting Project", + "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", + "name": "Abigail Lewis", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm_all_sites.json index 7f93a677c..475561bfb 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_lasso.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_lasso.json index 76103e4dd..14d52c8aa 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_lasso.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_lasso.json @@ -14,6 +14,70 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 + ], + [ + -84.4686, + 31.1948 + ], [ -106.8425, 32.5907 @@ -137,77 +201,13 @@ [ -110.5391, 44.9535 - ], - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 - ], - [ - -84.4686, - 31.1948 ] ] }, "properties": { "title": "tg_lasso", - "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -238,6 +238,22 @@ "rcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", "JORN", "KONA", "KONZ", @@ -268,23 +284,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC" + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm.json index c54883d63..3aba1f48b 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm.json @@ -14,66 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 - ], [ -84.4686, 31.1948 @@ -201,13 +141,73 @@ [ -110.5391, 44.9535 + ], + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 ] ] }, "properties": { "title": "tg_precip_lm", - "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -238,21 +238,6 @@ "rcc_90", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", "JERC", "JORN", "KONA", @@ -284,7 +269,22 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm_all_sites.json index dca4cdac3..23318850d 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm_all_sites.json @@ -14,6 +14,54 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], [ -66.8687, 17.9696 @@ -153,67 +201,19 @@ [ -110.5391, 44.9535 - ], - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 ] ] }, "properties": { "title": "tg_precip_lm_all_sites", - "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "name": "Gregory Harrison", "roles": [ "producer", "processor", @@ -238,6 +238,18 @@ "rcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", "GUAN", "HARV", "HEAL", @@ -272,19 +284,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM" + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_randfor.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_randfor.json index ca2c1839a..24b60600f 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_randfor.json @@ -14,70 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 - ], - [ - -84.4686, - 31.1948 - ], [ -106.8425, 32.5907 @@ -201,13 +137,77 @@ [ -110.5391, 44.9535 + ], + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 + ], + [ + -84.4686, + 31.1948 ] ] }, "properties": { "title": "tg_randfor", - "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -238,22 +238,6 @@ "rcc_90", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", "JORN", "KONA", "KONZ", @@ -284,7 +268,23 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json index 71ca5534c..60a79640e 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_tbats", "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm.json index 678fd6364..99e5ccd38 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_temp_lm", "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm_all_sites.json index 95def473e..f5b66b6ac 100644 --- a/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm_all_sites.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/30min_Net_ecosystem_exchange/models/climatology.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/30min_Net_ecosystem_exchange/models/climatology.json index 88aa44536..fd07db8c2 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/30min_Net_ecosystem_exchange/models/climatology.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/30min_Net_ecosystem_exchange/models/climatology.json @@ -207,7 +207,7 @@ "properties": { "title": "climatology", "description": "All forecasts for the 30min_Net_ecosystem_exchange variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-01-09T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/30min_latent_heat_flux/models/climatology.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/30min_latent_heat_flux/models/climatology.json index 2dad8572b..f165bda28 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/30min_latent_heat_flux/models/climatology.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/30min_latent_heat_flux/models/climatology.json @@ -207,7 +207,7 @@ "properties": { "title": "climatology", "description": "All forecasts for the 30min_latent_heat_flux variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: SJER, SOAP, SRER, STEI, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, STER, TALL, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-01-09T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/collection.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/collection.json index ea87a6941..ec1700be3 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/collection.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/collection.json @@ -11,67 +11,67 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/tg_ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/tg_temp_lm_all_sites.json" }, { "rel": "item", diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/USUNEEDAILY.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/USUNEEDAILY.json index 45ca31cb9..c1a789ba4 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/USUNEEDAILY.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/USUNEEDAILY.json @@ -23,7 +23,7 @@ "properties": { "title": "USUNEEDAILY", "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the USUNEEDAILY model. Information for the model is provided as follows: \"Home brew ARIMA.\" We didn't use a formal time series framework because of all the missing values in both our response variable and the weather covariates. So we used a GAM to fit a seasonal component based on day of year, and we included NEE the previous day as as an AR 1 term. We did some model selection, using cross validation, to identify temperature and relative humidity as weather covariates..\n The model predicts this variable at the following sites: PUUM.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-12-12T00:00:00Z", "end_datetime": "2024-01-16T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json index 3dabc2524..b7923b5ac 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json @@ -27,7 +27,7 @@ "properties": { "title": "bookcast_forest", "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the bookcast_forest model. Information for the model is provided as follows: A simple daily timestep process-based model of a terrestrial carbon cycle. It includes leaves, wood, and soil pools. It uses a light-use efficiency GPP model to convert PAR to carbon. The model is derived from https://github.com/mdietze/FluxCourseForecast..\n The model predicts this variable at the following sites: TALL, OSBS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-01-10T00:00:00Z", "end_datetime": "2024-07-12T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/cb_prophet.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/cb_prophet.json index 69fc2bbd7..70cf9d8a7 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/cb_prophet.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/cb_prophet.json @@ -203,7 +203,7 @@ "properties": { "title": "cb_prophet", "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: PUUM, GUAN, OSBS, SCBI, MOAB, BART, CPER, HARV, UNDE, STER, KONA, TREE, ABBY, LENO, UKFS, DEJU, KONZ, RMNP, BARR, JORN, SOAP, STEI, TALL, DCFS, TOOL, WOOD, OAES, HEAL, SERC, BLAN, GRSM, ORNL, SRER, NOGP, JERC, DELA, MLBS, NIWO, WREF, LAJA, TEAK, CLBJ, SJER, ONAQ, DSNY, BONA.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/climatology.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/climatology.json index 47037b591..6c82b573c 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/climatology.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/climatology.json @@ -207,7 +207,7 @@ "properties": { "title": "climatology", "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json index 844647aad..2d1a8f76d 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json @@ -207,7 +207,7 @@ "properties": { "title": "persistenceRW", "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: BARR, BART, BLAN, BONA, SJER, SOAP, SRER, STEI, STER, TALL, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, ABBY, TEAK, TOOL, TREE, UKFS, UNDE, DELA, DSNY, GRSM, GUAN, HARV, HEAL, CLBJ, CPER, DCFS, DEJU, WOOD, WREF, YELL, JERC, JORN, KONA, KONZ, PUUM, RMNP, SCBI, SERC.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json index 8ebbb2599..6cf9aee05 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json @@ -14,6 +14,78 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 + ], + [ + -84.4686, + 31.1948 + ], + [ + -106.8425, + 32.5907 + ], + [ + -96.6129, + 39.1104 + ], [ -96.5631, 39.1008 @@ -129,91 +201,19 @@ [ -110.5391, 44.9535 - ], - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 - ], - [ - -84.4686, - 31.1948 - ], - [ - -106.8425, - 32.5907 - ], - [ - -96.6129, - 39.1104 ] ] }, "properties": { "title": "tg_arima", - "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Gregory Harrison", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", @@ -238,6 +238,24 @@ "nee", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", + "KONA", "KONZ", "LAJA", "LENO", @@ -266,25 +284,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA" + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json index 0eb0a5f66..281863ca4 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_ets", "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm.json index 27f4eff85..5ac00c428 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_humidity_lm", "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ { - "url": "https://projects.ecoforecast.org/neon4cast-ci/", - "name": "NEON Ecological Forecasting Project", + "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", + "name": "Abigail Lewis", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm_all_sites.json index 58d9f8608..fca1f50ed 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm.json index 27d703390..2d62b6834 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_precip_lm", "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm_all_sites.json index 7722f95cc..7e93f1e9c 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm_all_sites.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "name": "Gregory Harrison", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_randfor.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_randfor.json index 26c3dba13..8b4e2d792 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_randfor.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_randfor", "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json index 57ae31a90..e8d45520c 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json @@ -14,6 +14,82 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -81.9934, + 29.6893 + ], + [ + -155.3173, + 19.5531 + ], + [ + -105.546, + 40.2759 + ], + [ + -78.1395, + 38.8929 + ], + [ + -76.56, + 38.8901 + ], + [ + -119.7323, + 37.1088 + ], + [ + -119.2622, + 37.0334 + ], + [ + -110.8355, + 31.9107 + ], + [ + -89.5864, + 45.5089 + ], + [ + -103.0293, + 40.4619 + ], + [ + -87.3933, + 32.9505 + ], + [ + -119.006, + 37.0058 + ], + [ + -149.3705, + 68.6611 + ], + [ + -89.5857, + 45.4937 + ], + [ + -95.1921, + 39.0404 + ], + [ + -89.5373, + 46.2339 + ], + [ + -99.2413, + 47.1282 + ], + [ + -121.9519, + 45.8205 + ], + [ + -110.5391, + 44.9535 + ], [ -122.3303, 45.7624 @@ -125,89 +201,13 @@ [ -84.2826, 35.9641 - ], - [ - -81.9934, - 29.6893 - ], - [ - -155.3173, - 19.5531 - ], - [ - -105.546, - 40.2759 - ], - [ - -78.1395, - 38.8929 - ], - [ - -76.56, - 38.8901 - ], - [ - -119.7323, - 37.1088 - ], - [ - -119.2622, - 37.0334 - ], - [ - -110.8355, - 31.9107 - ], - [ - -89.5864, - 45.5089 - ], - [ - -103.0293, - 40.4619 - ], - [ - -87.3933, - 32.9505 - ], - [ - -119.006, - 37.0058 - ], - [ - -149.3705, - 68.6611 - ], - [ - -89.5857, - 45.4937 - ], - [ - -95.1921, - 39.0404 - ], - [ - -89.5373, - 46.2339 - ], - [ - -99.2413, - 47.1282 - ], - [ - -121.9519, - 45.8205 - ], - [ - -110.5391, - 44.9535 ] ] }, "properties": { "title": "tg_tbats", - "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -238,6 +238,25 @@ "nee", "Daily", "P1D", + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -265,26 +284,7 @@ "NOGP", "OAES", "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "ORNL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm.json index 90c14df78..7c425e7b9 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_temp_lm", "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm_all_sites.json index 8d14099b0..78233db93 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm_all_sites.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/collection.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/collection.json index a78d11eee..9874f0ec5 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/collection.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/collection.json @@ -16,57 +16,57 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_temp_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/tg_ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "parent", diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/cb_prophet.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/cb_prophet.json index 9673b7996..2a24102d8 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/cb_prophet.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/cb_prophet.json @@ -203,7 +203,7 @@ "properties": { "title": "cb_prophet", "description": "All forecasts for the Daily_latent_heat_flux variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: CLBJ, SJER, ONAQ, DSNY, SCBI, MOAB, PUUM, GUAN, OSBS, BART, CPER, HARV, UNDE, STER, KONA, TREE, ABBY, LENO, UKFS, DEJU, KONZ, RMNP, BARR, JORN, SOAP, STEI, TALL, DCFS, TOOL, WOOD, OAES, HEAL, SERC, BLAN, GRSM, ORNL, SRER, NOGP, JERC, DELA, MLBS, NIWO, WREF, LAJA, TEAK, BONA.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/climatology.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/climatology.json index 982e8c982..e2bceae2e 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/climatology.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/climatology.json @@ -207,7 +207,7 @@ "properties": { "title": "climatology", "description": "All forecasts for the Daily_latent_heat_flux variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json index ad4109c95..cd8f67b86 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json @@ -14,58 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -119.2622, - 37.0334 - ], - [ - -110.8355, - 31.9107 - ], - [ - -89.5864, - 45.5089 - ], - [ - -103.0293, - 40.4619 - ], - [ - -87.3933, - 32.9505 - ], - [ - -119.006, - 37.0058 - ], - [ - -149.3705, - 68.6611 - ], - [ - -89.5857, - 45.4937 - ], - [ - -95.1921, - 39.0404 - ], - [ - -89.5373, - 46.2339 - ], - [ - -99.2413, - 47.1282 - ], - [ - -121.9519, - 45.8205 - ], - [ - -110.5391, - 44.9535 - ], [ -122.3303, 45.7624 @@ -201,19 +149,71 @@ [ -119.7323, 37.1088 + ], + [ + -119.2622, + 37.0334 + ], + [ + -110.8355, + 31.9107 + ], + [ + -89.5864, + 45.5089 + ], + [ + -103.0293, + 40.4619 + ], + [ + -87.3933, + 32.9505 + ], + [ + -119.006, + 37.0058 + ], + [ + -149.3705, + 68.6611 + ], + [ + -89.5857, + 45.4937 + ], + [ + -95.1921, + 39.0404 + ], + [ + -89.5373, + 46.2339 + ], + [ + -99.2413, + 47.1282 + ], + [ + -121.9519, + 45.8205 + ], + [ + -110.5391, + 44.9535 ] ] }, "properties": { "title": "tg_arima", - "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Gregory Harrison", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", @@ -238,19 +238,6 @@ "le", "Daily", "P1D", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -284,7 +271,20 @@ "RMNP", "SCBI", "SERC", - "SJER" + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json index a02c4ccb7..7da5beb42 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_ets", "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm.json index e9bfedf6b..8751bab64 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm.json @@ -14,6 +14,18 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 + ], + [ + -84.4686, + 31.1948 + ], [ -122.3303, 45.7624 @@ -66,18 +78,6 @@ -66.8687, 17.9696 ], - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 - ], - [ - -84.4686, - 31.1948 - ], [ -106.8425, 32.5907 @@ -206,14 +206,14 @@ }, "properties": { "title": "tg_humidity_lm", - "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: HARV, HEAL, JERC, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ { - "url": "https://projects.ecoforecast.org/neon4cast-ci/", - "name": "NEON Ecological Forecasting Project", + "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", + "name": "Abigail Lewis", "roles": [ "producer", "processor", @@ -238,6 +238,9 @@ "le", "Daily", "P1D", + "HARV", + "HEAL", + "JERC", "ABBY", "BARR", "BART", @@ -251,9 +254,6 @@ "DSNY", "GRSM", "GUAN", - "HARV", - "HEAL", - "JERC", "JORN", "KONA", "KONZ", diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm_all_sites.json index 4bff101da..7665d12b8 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm.json index 3c0460096..5dcf81f09 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm.json @@ -14,6 +14,66 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 + ], [ -84.4686, 31.1948 @@ -141,73 +201,13 @@ [ -110.5391, 44.9535 - ], - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 ] ] }, "properties": { "title": "tg_precip_lm", - "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -238,6 +238,21 @@ "le", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", "JERC", "JORN", "KONA", @@ -269,22 +284,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL" + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm_all_sites.json index 995b1bfab..b46e78deb 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm_all_sites.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "name": "Gregory Harrison", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_randfor.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_randfor.json index d9def00ef..58263191d 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_randfor.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_randfor", "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json index 32b169df7..186ccf33b 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json @@ -14,94 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 - ], - [ - -84.4686, - 31.1948 - ], - [ - -106.8425, - 32.5907 - ], - [ - -96.6129, - 39.1104 - ], - [ - -96.5631, - 39.1008 - ], - [ - -67.0769, - 18.0213 - ], - [ - -88.1612, - 31.8539 - ], - [ - -80.5248, - 37.3783 - ], [ -109.3883, 38.2483 @@ -201,13 +113,101 @@ [ -110.5391, 44.9535 + ], + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 + ], + [ + -84.4686, + 31.1948 + ], + [ + -106.8425, + 32.5907 + ], + [ + -96.6129, + 39.1104 + ], + [ + -96.5631, + 39.1008 + ], + [ + -67.0769, + 18.0213 + ], + [ + -88.1612, + 31.8539 + ], + [ + -80.5248, + 37.3783 ] ] }, "properties": { "title": "tg_tbats", - "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -238,28 +238,6 @@ "le", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA", - "KONZ", - "LAJA", - "LENO", - "MLBS", "MOAB", "NIWO", "NOGP", @@ -284,7 +262,29 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", + "KONA", + "KONZ", + "LAJA", + "LENO", + "MLBS" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm.json index e15213aee..dc4a8a75c 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_temp_lm", "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm_all_sites.json index e6727a218..b00d54041 100644 --- a/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm_all_sites.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/collection.json b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/collection.json index 7f621db2b..72501a130 100644 --- a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/collection.json +++ b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/collection.json @@ -21,17 +21,17 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/tg_lasso.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", @@ -41,22 +41,22 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_lasso.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_temp_lm_all_sites.json" }, { "rel": "item", diff --git a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_arima.json b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_arima.json index cf6a4fb5f..1ce4fa420 100644 --- a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_arima.json @@ -14,6 +14,18 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -78.0418, + 39.0337 + ], + [ + -96.5631, + 39.1008 + ], + [ + -88.1612, + 31.8539 + ], [ -84.2826, 35.9641 @@ -37,31 +49,19 @@ [ -95.1921, 39.0404 - ], - [ - -78.0418, - 39.0337 - ], - [ - -96.5631, - 39.1008 - ], - [ - -88.1612, - 31.8539 ] ] }, "properties": { "title": "tg_arima", - "description": "All forecasts for the Weekly_Amblyomma_americanum_population variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ORNL, OSBS, SCBI, SERC, TALL, UKFS, BLAN, KONZ, LENO.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Weekly_Amblyomma_americanum_population variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-02-13T00:00:00Z", "end_datetime": "2025-06-23T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Gregory Harrison", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", @@ -86,15 +86,15 @@ "amblyomma_americanum", "Weekly", "P1W", + "BLAN", + "KONZ", + "LENO", "ORNL", "OSBS", "SCBI", "SERC", "TALL", - "UKFS", - "BLAN", - "KONZ", - "LENO" + "UKFS" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_ets.json b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_ets.json index c30d20b52..39567c026 100644 --- a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_ets.json @@ -55,7 +55,7 @@ "properties": { "title": "tg_ets", "description": "All forecasts for the Weekly_Amblyomma_americanum_population variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-02-06T00:00:00Z", "end_datetime": "2025-06-23T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm.json index f6fd087da..5f4b7b88d 100644 --- a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm.json @@ -55,13 +55,13 @@ "properties": { "title": "tg_humidity_lm", "description": "All forecasts for the Weekly_Amblyomma_americanum_population variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ { - "url": "https://projects.ecoforecast.org/neon4cast-ci/", - "name": "NEON Ecological Forecasting Project", + "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", + "name": "Abigail Lewis", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm_all_sites.json index 8e13fd098..6ce392c9c 100644 --- a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm_all_sites.json @@ -55,7 +55,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All forecasts for the Weekly_Amblyomma_americanum_population variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_lasso.json b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_lasso.json index 8db548095..526395366 100644 --- a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_lasso.json +++ b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_lasso.json @@ -51,7 +51,7 @@ "properties": { "title": "tg_lasso", "description": "All forecasts for the Weekly_Amblyomma_americanum_population variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: BLAN, KONZ, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm.json index 9d3bc1bca..cae715425 100644 --- a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm.json @@ -55,7 +55,7 @@ "properties": { "title": "tg_precip_lm", "description": "All forecasts for the Weekly_Amblyomma_americanum_population variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm_all_sites.json index 680c64100..3be61ae21 100644 --- a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm_all_sites.json @@ -55,13 +55,13 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All forecasts for the Weekly_Amblyomma_americanum_population variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "name": "Gregory Harrison", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_randfor.json b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_randfor.json index 8fd04703d..14f97aa4f 100644 --- a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_randfor.json @@ -51,7 +51,7 @@ "properties": { "title": "tg_randfor", "description": "All forecasts for the Weekly_Amblyomma_americanum_population variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: BLAN, KONZ, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-19T00:00:00Z", "end_datetime": "2024-03-01T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_tbats.json b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_tbats.json index b32e9c0df..765df1dd8 100644 --- a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_tbats.json @@ -55,7 +55,7 @@ "properties": { "title": "tg_tbats", "description": "All forecasts for the Weekly_Amblyomma_americanum_population variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2025-06-23T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm.json index 3d3399e46..27102f209 100644 --- a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm.json @@ -55,7 +55,7 @@ "properties": { "title": "tg_temp_lm", "description": "All forecasts for the Weekly_Amblyomma_americanum_population variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm_all_sites.json index 7d23c6009..0546a30eb 100644 --- a/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/forecasts/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm_all_sites.json @@ -55,13 +55,13 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All forecasts for the Weekly_Amblyomma_americanum_population variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/noaa_forecasts/Pseudo/collection.json b/data/challenge/neon4cast-stac/noaa_forecasts/Pseudo/collection.json index d57442cfc..4fed43fcf 100644 --- a/data/challenge/neon4cast-stac/noaa_forecasts/Pseudo/collection.json +++ b/data/challenge/neon4cast-stac/noaa_forecasts/Pseudo/collection.json @@ -58,7 +58,7 @@ "interval": [ [ "2020-01-01T00:00:00Z", - "2024-09-06T00:00:00Z" + "2024-09-07T00:00:00Z" ] ] } diff --git a/data/challenge/neon4cast-stac/noaa_forecasts/Stage1-stats/collection.json b/data/challenge/neon4cast-stac/noaa_forecasts/Stage1-stats/collection.json index 355e84ed5..26345827c 100644 --- a/data/challenge/neon4cast-stac/noaa_forecasts/Stage1-stats/collection.json +++ b/data/challenge/neon4cast-stac/noaa_forecasts/Stage1-stats/collection.json @@ -58,7 +58,7 @@ "interval": [ [ "2020-01-01T00:00:00Z", - "2024-09-06T00:00:00Z" + "2024-09-07T00:00:00Z" ] ] } diff --git a/data/challenge/neon4cast-stac/noaa_forecasts/Stage1/collection.json b/data/challenge/neon4cast-stac/noaa_forecasts/Stage1/collection.json index 57f884a82..71bb2cb73 100644 --- a/data/challenge/neon4cast-stac/noaa_forecasts/Stage1/collection.json +++ b/data/challenge/neon4cast-stac/noaa_forecasts/Stage1/collection.json @@ -58,7 +58,7 @@ "interval": [ [ "2020-01-01T00:00:00Z", - "2024-09-06T00:00:00Z" + "2024-09-07T00:00:00Z" ] ] } diff --git a/data/challenge/neon4cast-stac/noaa_forecasts/Stage2/collection.json b/data/challenge/neon4cast-stac/noaa_forecasts/Stage2/collection.json index 640c98ffc..d2d42c986 100644 --- a/data/challenge/neon4cast-stac/noaa_forecasts/Stage2/collection.json +++ b/data/challenge/neon4cast-stac/noaa_forecasts/Stage2/collection.json @@ -58,7 +58,7 @@ "interval": [ [ "2020-01-01T00:00:00Z", - "2024-09-06T00:00:00Z" + "2024-09-07T00:00:00Z" ] ] } diff --git a/data/challenge/neon4cast-stac/noaa_forecasts/Stage3/collection.json b/data/challenge/neon4cast-stac/noaa_forecasts/Stage3/collection.json index 0322321cb..8895500ab 100644 --- a/data/challenge/neon4cast-stac/noaa_forecasts/Stage3/collection.json +++ b/data/challenge/neon4cast-stac/noaa_forecasts/Stage3/collection.json @@ -58,7 +58,7 @@ "interval": [ [ "2020-01-01T00:00:00Z", - "2024-09-06T00:00:00Z" + "2024-09-07T00:00:00Z" ] ] } diff --git a/data/challenge/neon4cast-stac/noaa_forecasts/collection.json b/data/challenge/neon4cast-stac/noaa_forecasts/collection.json index c435bdf60..5f5dabe0f 100644 --- a/data/challenge/neon4cast-stac/noaa_forecasts/collection.json +++ b/data/challenge/neon4cast-stac/noaa_forecasts/collection.json @@ -86,7 +86,7 @@ "interval": [ [ "2020-01-01T00:00:00Z", - "2024-09-06T00:00:00Z" + "2024-09-07T00:00:00Z" ] ] } diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/collection.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/collection.json index fcc89a207..d49a37b0a 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/collection.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/collection.json @@ -8,11 +8,6 @@ ], "type": "Collection", "links": [ - { - "rel": "item", - "type": "application/json", - "href": "./models/tg_tbats.json" - }, { "rel": "item", "type": "application/json", @@ -33,6 +28,11 @@ "type": "application/json", "href": "./models/tg_ets.json" }, + { + "rel": "item", + "type": "application/json", + "href": "./models/tg_tbats.json" + }, { "rel": "parent", "type": "application/json", diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/climatology.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/climatology.json index 03e6531d9..9da5bf602 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/climatology.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/climatology.json @@ -59,7 +59,7 @@ "properties": { "title": "climatology", "description": "All scores for the Daily_Chlorophyll_a variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRPO, SUGG, TOMB, PRLA, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-07T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json index ac2d35675..f35a6c75f 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json @@ -59,7 +59,7 @@ "properties": { "title": "persistenceRW", "description": "All scores for the Daily_Chlorophyll_a variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json index 26e0aab19..c4db6cad9 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json @@ -59,13 +59,13 @@ "properties": { "title": "tg_arima", "description": "All scores for the Daily_Chlorophyll_a variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Gregory Harrison", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json index 14dc00cc7..2133e8697 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_ets", "description": "All scores for the Daily_Chlorophyll_a variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json index 5dc1e5daa..5f7a7b49a 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json @@ -14,18 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -82.0177, - 29.6878 - ], - [ - -88.1589, - 31.8534 - ], - [ - -149.6106, - 68.6307 - ], [ -82.0084, 29.676 @@ -53,13 +41,25 @@ [ -99.2531, 47.1298 + ], + [ + -82.0177, + 29.6878 + ], + [ + -88.1589, + 31.8534 + ], + [ + -149.6106, + 68.6307 ] ] }, "properties": { "title": "tg_tbats", - "description": "All scores for the Daily_Chlorophyll_a variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: SUGG, TOMB, TOOK, BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All scores for the Daily_Chlorophyll_a variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -90,16 +90,16 @@ "chla", "Daily", "P1D", - "SUGG", - "TOMB", - "TOOK", "BARC", "BLWA", "CRAM", "FLNT", "LIRO", "PRLA", - "PRPO" + "PRPO", + "SUGG", + "TOMB", + "TOOK" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/collection.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/collection.json index 13726e37c..99eeb51e2 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/collection.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/collection.json @@ -11,12 +11,12 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/hotdeck.json" }, { "rel": "item", @@ -26,22 +26,22 @@ { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/AquaticEcosystemsOxygen.json" }, { "rel": "item", "type": "application/json", - "href": "./models/hotdeck.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/tg_ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/AquaticEcosystemsOxygen.json" + "href": "./models/tg_tbats.json" }, { "rel": "parent", diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json index b5dee09f6..d117a6ff7 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json @@ -23,7 +23,7 @@ "properties": { "title": "AquaticEcosystemsOxygen", "description": "All scores for the Daily_Dissolved_oxygen variable for the AquaticEcosystemsOxygen model. Information for the model is provided as follows: Used a Bayesian Dynamic Linear Model using the fit_dlm function from the ecoforecastR package.\n The model predicts this variable at the following sites: WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-07-31T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/climatology.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/climatology.json index 305fa5d62..231a93241 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/climatology.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/climatology.json @@ -155,7 +155,7 @@ "properties": { "title": "climatology", "description": "All scores for the Daily_Dissolved_oxygen variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, OKSR.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-07T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json index 00b9f9ad7..dbbb0b2f5 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json @@ -75,7 +75,7 @@ "properties": { "title": "hotdeck", "description": "All scores for the Daily_Dissolved_oxygen variable for the hotdeck model. Information for the model is provided as follows: Uses a hot deck approach: - Take the latest observation/forecast. - Past observations from around the same window of the season are collected. - Values close to the latest observation/forecast are collected. - One of these is randomly sampled. - Its \"tomorrow\" observation is used as the forecast. - Repeat until forecast at step h..\n The model predicts this variable at the following sites: BARC, BIGC, BLDE, CRAM, KING, LEWI, LIRO, MAYF, MCRA, POSE, PRIN, REDB, SUGG, SYCA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json index ee926820b..7c1823d65 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json @@ -155,7 +155,7 @@ "properties": { "title": "persistenceRW", "description": "All scores for the Daily_Dissolved_oxygen variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json index 0f2cf3f03..f1e28ba48 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json @@ -14,6 +14,54 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -78.1473, + 38.8943 + ], + [ + -97.7823, + 33.3785 + ], + [ + -99.1139, + 47.1591 + ], + [ + -99.2531, + 47.1298 + ], + [ + -111.7979, + 40.7839 + ], + [ + -82.0177, + 29.6878 + ], + [ + -111.5081, + 33.751 + ], + [ + -119.0274, + 36.9559 + ], + [ + -88.1589, + 31.8534 + ], + [ + -149.6106, + 68.6307 + ], + [ + -84.2793, + 35.9574 + ], + [ + -105.9154, + 39.8914 + ], [ -102.4471, 39.7582 @@ -101,67 +149,19 @@ [ -149.143, 68.6698 - ], - [ - -78.1473, - 38.8943 - ], - [ - -97.7823, - 33.3785 - ], - [ - -99.1139, - 47.1591 - ], - [ - -99.2531, - 47.1298 - ], - [ - -111.7979, - 40.7839 - ], - [ - -82.0177, - 29.6878 - ], - [ - -111.5081, - 33.751 - ], - [ - -119.0274, - 36.9559 - ], - [ - -88.1589, - 31.8534 - ], - [ - -149.6106, - 68.6307 - ], - [ - -84.2793, - 35.9574 - ], - [ - -105.9154, - 39.8914 ] ] }, "properties": { "title": "tg_arima", - "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Gregory Harrison", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", @@ -186,6 +186,18 @@ "oxygen", "Daily", "P1D", + "POSE", + "PRIN", + "PRLA", + "PRPO", + "REDB", + "SUGG", + "SYCA", + "TECR", + "TOMB", + "TOOK", + "WALK", + "WLOU", "ARIK", "BARC", "BIGC", @@ -207,19 +219,7 @@ "MAYF", "MCDI", "MCRA", - "OKSR", - "POSE", - "PRIN", - "PRLA", - "PRPO", - "REDB", - "SUGG", - "SYCA", - "TECR", - "TOMB", - "TOOK", - "WALK", - "WLOU" + "OKSR" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json index 16f8ccf0d..91fd2b409 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json @@ -14,6 +14,62 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -102.4471, + 39.7582 + ], + [ + -82.0084, + 29.676 + ], + [ + -119.2575, + 37.0597 + ], + [ + -110.5871, + 44.9501 + ], + [ + -96.6242, + 34.4442 + ], + [ + -87.7982, + 32.5415 + ], + [ + -147.504, + 65.1532 + ], + [ + -105.5442, + 40.035 + ], + [ + -89.4737, + 46.2097 + ], + [ + -66.9868, + 18.1135 + ], + [ + -84.4374, + 31.1854 + ], + [ + -66.7987, + 18.1741 + ], + [ + -72.3295, + 42.4719 + ], + [ + -96.6038, + 39.1051 + ], [ -83.5038, 35.6904 @@ -93,69 +149,13 @@ [ -105.9154, 39.8914 - ], - [ - -102.4471, - 39.7582 - ], - [ - -82.0084, - 29.676 - ], - [ - -119.2575, - 37.0597 - ], - [ - -110.5871, - 44.9501 - ], - [ - -96.6242, - 34.4442 - ], - [ - -87.7982, - 32.5415 - ], - [ - -147.504, - 65.1532 - ], - [ - -105.5442, - 40.035 - ], - [ - -89.4737, - 46.2097 - ], - [ - -66.9868, - 18.1135 - ], - [ - -84.4374, - 31.1854 - ], - [ - -66.7987, - 18.1741 - ], - [ - -72.3295, - 42.4719 - ], - [ - -96.6038, - 39.1051 ] ] }, "properties": { "title": "tg_ets", - "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -186,6 +186,20 @@ "oxygen", "Daily", "P1D", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", + "BLWA", + "CARI", + "COMO", + "CRAM", + "CUPE", + "FLNT", + "GUIL", + "HOPB", + "KING", "LECO", "LEWI", "LIRO", @@ -205,21 +219,7 @@ "TOMB", "TOOK", "WALK", - "WLOU", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", - "BLWA", - "CARI", - "COMO", - "CRAM", - "CUPE", - "FLNT", - "GUIL", - "HOPB", - "KING" + "WLOU" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json index fd96e45ba..80eb5f226 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json @@ -14,30 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -102.4471, - 39.7582 - ], - [ - -82.0084, - 29.676 - ], - [ - -119.2575, - 37.0597 - ], - [ - -110.5871, - 44.9501 - ], - [ - -96.6242, - 34.4442 - ], - [ - -87.7982, - 32.5415 - ], [ -147.504, 65.1532 @@ -149,13 +125,37 @@ [ -105.9154, 39.8914 + ], + [ + -102.4471, + 39.7582 + ], + [ + -82.0084, + 29.676 + ], + [ + -119.2575, + 37.0597 + ], + [ + -110.5871, + 44.9501 + ], + [ + -96.6242, + 34.4442 + ], + [ + -87.7982, + 32.5415 ] ] }, "properties": { "title": "tg_tbats", - "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -186,12 +186,6 @@ "oxygen", "Daily", "P1D", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", - "BLWA", "CARI", "COMO", "CRAM", @@ -219,7 +213,13 @@ "TOMB", "TOOK", "WALK", - "WLOU" + "WLOU", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", + "BLWA" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/collection.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/collection.json index 53848dfa3..b7ee05b36 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/collection.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/collection.json @@ -11,47 +11,47 @@ { "rel": "item", "type": "application/json", - "href": "./models/GLEON_JRabaey_temp_physics.json" + "href": "./models/fARIMA_clim_ensemble.json" }, { "rel": "item", "type": "application/json", - "href": "./models/GLEON_lm_lag_1day.json" + "href": "./models/fTSLM_lag.json" }, { "rel": "item", "type": "application/json", - "href": "./models/baseline_ensemble.json" + "href": "./models/GLEON_JRabaey_temp_physics.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fARIMA_clim_ensemble.json" + "href": "./models/GLEON_lm_lag_1day.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/baseline_ensemble.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fTSLM_lag.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flareGLM.json" + "href": "./models/tg_ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flareGLM_noDA.json" + "href": "./models/GAM_air_wind.json" }, { "rel": "item", "type": "application/json", - "href": "./models/GAM_air_wind.json" + "href": "./models/flareGLM_noDA.json" }, { "rel": "item", @@ -61,47 +61,47 @@ { "rel": "item", "type": "application/json", - "href": "./models/bee_bake_RFModel_2024.json" + "href": "./models/flareGLM.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/bee_bake_RFModel_2024.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/lm_AT_WTL_WS.json" + "href": "./models/zimmerman_proj1.json" }, { "rel": "item", "type": "application/json", - "href": "./models/mkricheldorf_w_lag.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/mlp1_wtempforecast_LF.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/lm_AT_WTL_WS.json" }, { "rel": "item", "type": "application/json", - "href": "./models/zimmerman_proj1.json" + "href": "./models/mkricheldorf_w_lag.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/mlp1_wtempforecast_LF.json" }, { "rel": "parent", diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/GAM_air_wind.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/GAM_air_wind.json index b68688906..67b0f873c 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/GAM_air_wind.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/GAM_air_wind.json @@ -47,7 +47,7 @@ "properties": { "title": "GAM_air_wind", "description": "All scores for the Daily_Water_temperature variable for the GAM_air_wind model. Information for the model is provided as follows: I used a GAM (mgcv) with a linear relationship to air temperature and smoothing for eastward and northward winds..\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-01T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json index 47374654d..067fe6f63 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json @@ -155,7 +155,7 @@ "properties": { "title": "GLEON_JRabaey_temp_physics", "description": "All scores for the Daily_Water_temperature variable for the GLEON_JRabaey_temp_physics model. Information for the model is provided as follows: The JR-physics model is a simple process model based on the assumption that surface water\ntemperature should trend towards equilibration with air temperature with a lag factor..\n The model predicts this variable at the following sites: MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-01-02T00:00:00Z", "end_datetime": "2024-03-12T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json index d7b09869c..8123d42f2 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json @@ -47,7 +47,7 @@ "properties": { "title": "GLEON_lm_lag_1day", "description": "All scores for the Daily_Water_temperature variable for the GLEON_lm_lag_1day model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-01-02T00:00:00Z", "end_datetime": "2024-02-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json index 5e93a4864..03aad42e1 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json @@ -155,7 +155,7 @@ "properties": { "title": "baseline_ensemble", "description": "All scores for the Daily_Water_temperature variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-01T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/bee_bake_RFModel_2024.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/bee_bake_RFModel_2024.json index 79443a0a7..d0d8e3bfa 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/bee_bake_RFModel_2024.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/bee_bake_RFModel_2024.json @@ -47,7 +47,7 @@ "properties": { "title": "bee_bake_RFModel_2024", "description": "All scores for the Daily_Water_temperature variable for the bee_bake_RFModel_2024 model. Information for the model is provided as follows: Random Forest.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/climatology.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/climatology.json index dc1167cfc..c8dc4aeef 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/climatology.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/climatology.json @@ -14,22 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -119.0274, - 36.9559 - ], - [ - -88.1589, - 31.8534 - ], - [ - -84.2793, - 35.9574 - ], - [ - -105.9154, - 39.8914 - ], [ -102.4471, 39.7582 @@ -146,6 +130,22 @@ -111.5081, 33.751 ], + [ + -119.0274, + 36.9559 + ], + [ + -88.1589, + 31.8534 + ], + [ + -84.2793, + 35.9574 + ], + [ + -105.9154, + 39.8914 + ], [ -149.6106, 68.6307 @@ -154,8 +154,8 @@ }, "properties": { "title": "climatology", - "description": "All scores for the Daily_Water_temperature variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: TECR, TOMB, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-07T00:00:00Z", "providers": [ @@ -186,10 +186,6 @@ "temperature", "Daily", "P1D", - "TECR", - "TOMB", - "WALK", - "WLOU", "ARIK", "BARC", "BIGC", @@ -219,6 +215,10 @@ "REDB", "SUGG", "SYCA", + "TECR", + "TOMB", + "WALK", + "WLOU", "TOOK" ], "table:columns": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json index cadd56d1e..e0b4e0ad1 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json @@ -15,12 +15,32 @@ "type": "MultiPoint", "coordinates": [ [ - -66.7987, - 18.1741 + -110.5871, + 44.9501 ], [ - -72.3295, - 42.4719 + -147.504, + 65.1532 + ], + [ + -105.5442, + 40.035 + ], + [ + -89.4737, + 46.2097 + ], + [ + -66.9868, + 18.1135 + ], + [ + -84.4374, + 31.1854 + ], + [ + -66.7987, + 18.1741 ], [ -96.6038, @@ -74,6 +94,10 @@ -99.2531, 47.1298 ], + [ + -111.7979, + 40.7839 + ], [ -82.0177, 29.6878 @@ -82,10 +106,6 @@ -111.5081, 33.751 ], - [ - -119.0274, - 36.9559 - ], [ -88.1589, 31.8534 @@ -107,51 +127,31 @@ 29.676 ], [ - -110.5871, - 44.9501 - ], - [ - -87.7982, - 32.5415 - ], - [ - -147.504, - 65.1532 - ], - [ - -105.5442, - 40.035 - ], - [ - -89.4737, - 46.2097 - ], - [ - -66.9868, - 18.1135 + -119.2575, + 37.0597 ], [ - -84.4374, - 31.1854 + -72.3295, + 42.4719 ], [ - -111.7979, - 40.7839 + -149.6106, + 68.6307 ], [ - -119.2575, - 37.0597 + -119.0274, + 36.9559 ], [ - -149.6106, - 68.6307 + -87.7982, + 32.5415 ] ] }, "properties": { "title": "fARIMA_clim_ensemble", - "description": "All scores for the Daily_Water_temperature variable for the fARIMA_clim_ensemble model. Information for the model is provided as follows: The fAMIRA-DOY MME is a multi-model ensemble (MME) composed of two empirical\nmodels: an ARIMA model (fARIMA) and day-of-year model.\n The model predicts this variable at the following sites: GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, SUGG, SYCA, TECR, TOMB, WALK, WLOU, ARIK, BARC, BLDE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, REDB, BIGC, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the fARIMA_clim_ensemble model. Information for the model is provided as follows: The fAMIRA-DOY MME is a multi-model ensemble (MME) composed of two empirical\nmodels: an ARIMA model (fARIMA) and day-of-year model.\n The model predicts this variable at the following sites: BLDE, CARI, COMO, CRAM, CUPE, FLNT, GUIL, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TOMB, WALK, WLOU, ARIK, BARC, BIGC, HOPB, TOOK, TECR, BLWA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-01T00:00:00Z", "providers": [ @@ -182,8 +182,13 @@ "temperature", "Daily", "P1D", + "BLDE", + "CARI", + "COMO", + "CRAM", + "CUPE", + "FLNT", "GUIL", - "HOPB", "KING", "LECO", "LEWI", @@ -197,24 +202,19 @@ "PRIN", "PRLA", "PRPO", + "REDB", "SUGG", "SYCA", - "TECR", "TOMB", "WALK", "WLOU", "ARIK", "BARC", - "BLDE", - "BLWA", - "CARI", - "COMO", - "CRAM", - "CUPE", - "FLNT", - "REDB", "BIGC", - "TOOK" + "HOPB", + "TOOK", + "TECR", + "BLWA" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json index b81ce4e83..ae727f455 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json @@ -14,6 +14,42 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -102.4471, + 39.7582 + ], + [ + -82.0084, + 29.676 + ], + [ + -119.2575, + 37.0597 + ], + [ + -110.5871, + 44.9501 + ], + [ + -96.6242, + 34.4442 + ], + [ + -87.7982, + 32.5415 + ], + [ + -147.504, + 65.1532 + ], + [ + -105.5442, + 40.035 + ], + [ + -89.4737, + 46.2097 + ], [ -66.9868, 18.1135 @@ -113,49 +149,13 @@ [ -105.9154, 39.8914 - ], - [ - -102.4471, - 39.7582 - ], - [ - -82.0084, - 29.676 - ], - [ - -119.2575, - 37.0597 - ], - [ - -110.5871, - 44.9501 - ], - [ - -96.6242, - 34.4442 - ], - [ - -87.7982, - 32.5415 - ], - [ - -147.504, - 65.1532 - ], - [ - -105.5442, - 40.035 - ], - [ - -89.4737, - 46.2097 ] ] }, "properties": { "title": "fTSLM_lag", - "description": "All scores for the Daily_Water_temperature variable for the fTSLM_lag model. Information for the model is provided as follows: This is a simple time series linear model in which water temperature is a function of air\ntemperature of that day and the previous day\u2019s air temperature.\n The model predicts this variable at the following sites: CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the fTSLM_lag model. Information for the model is provided as follows: This is a simple time series linear model in which water temperature is a function of air\ntemperature of that day and the previous day\u2019s air temperature.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ @@ -186,6 +186,15 @@ "temperature", "Daily", "P1D", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", + "BLWA", + "CARI", + "COMO", + "CRAM", "CUPE", "FLNT", "GUIL", @@ -210,16 +219,7 @@ "TOMB", "TOOK", "WALK", - "WLOU", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", - "BLWA", - "CARI", - "COMO", - "CRAM" + "WLOU" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/flareGLM.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/flareGLM.json index c67605051..875f69dd6 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/flareGLM.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/flareGLM.json @@ -47,7 +47,7 @@ "properties": { "title": "flareGLM", "description": "All scores for the Daily_Water_temperature variable for the flareGLM model. Information for the model is provided as follows: The FLARE-GLM is a forecasting framework that integrates the General Lake Model\nhydrodynamic process model (GLM; Hipsey et al., 2019) and data assimilation algorithm to generate\nensemble forecasts of lake water temperature..\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json index 0ac8c6683..fda37f6d9 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json @@ -14,18 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -82.0084, - 29.676 - ], - [ - -89.4737, - 46.2097 - ], - [ - -89.7048, - 45.9983 - ], [ -99.1139, 47.1591 @@ -41,19 +29,31 @@ [ -149.6106, 68.6307 + ], + [ + -82.0084, + 29.676 + ], + [ + -89.4737, + 46.2097 + ], + [ + -89.7048, + 45.9983 ] ] }, "properties": { "title": "flareGLM_noDA", - "description": "All scores for the Daily_Water_temperature variable for the flareGLM_noDA model. Information for the model is provided as follows: The FLARE-GLM is a forecasting framework that integrates the General Lake Model\nhydrodynamic process model (GLM; Hipsey et al., 2019). This version does not incorportate data assimilation.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the flareGLM_noDA model. Information for the model is provided as follows: The FLARE-GLM is a forecasting framework that integrates the General Lake Model\nhydrodynamic process model (GLM; Hipsey et al., 2019). This version does not incorportate data assimilation.\n The model predicts this variable at the following sites: PRLA, PRPO, SUGG, TOOK, BARC, CRAM, LIRO.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ { "url": "https://github.com/FLARE-forecast/NEON-forecast-code/workflows/default", - "name": "Freya Olsson", + "name": "Joseph Rabaey", "roles": [ "producer", "processor", @@ -78,13 +78,13 @@ "temperature", "Daily", "P1D", - "BARC", - "CRAM", - "LIRO", "PRLA", "PRPO", "SUGG", - "TOOK" + "TOOK", + "BARC", + "CRAM", + "LIRO" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/hotdeck.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/hotdeck.json index 365e6962c..135011d30 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/hotdeck.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/hotdeck.json @@ -139,7 +139,7 @@ "properties": { "title": "hotdeck", "description": "All scores for the Daily_Water_temperature variable for the hotdeck model. Information for the model is provided as follows: Uses a hot deck approach: - Take the latest observation/forecast. - Past observations from around the same window of the season are collected. - Values close to the latest observation/forecast are collected. - One of these is randomly sampled. - Its \"tomorrow\" observation is used as the forecast. - Repeat until forecast at step h..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MAYF, MCRA, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/lm_AT_WTL_WS.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/lm_AT_WTL_WS.json index d953a95c6..330787c4c 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/lm_AT_WTL_WS.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/lm_AT_WTL_WS.json @@ -47,7 +47,7 @@ "properties": { "title": "lm_AT_WTL_WS", "description": "All scores for the Daily_Water_temperature variable for the lm_AT_WTL_WS model. Information for the model is provided as follows: This forecast of water temperature at NEON Lake sites uses a linear model, incorporating air temperature, wind speed, and the previous day's forecasted water temperature as variables..\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/mkricheldorf_w_lag.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/mkricheldorf_w_lag.json index 90b0dbcc2..ed545a85e 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/mkricheldorf_w_lag.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/mkricheldorf_w_lag.json @@ -47,7 +47,7 @@ "properties": { "title": "mkricheldorf_w_lag", "description": "All scores for the Daily_Water_temperature variable for the mkricheldorf_w_lag model. Information for the model is provided as follows: I used an autoregressive linear model using the lm() function.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-26T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/mlp1_wtempforecast_LF.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/mlp1_wtempforecast_LF.json index 08563a570..ee2d8a6c9 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/mlp1_wtempforecast_LF.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/mlp1_wtempforecast_LF.json @@ -47,7 +47,7 @@ "properties": { "title": "mlp1_wtempforecast_LF", "description": "All scores for the Daily_Water_temperature variable for the mlp1_wtempforecast_LF model. Information for the model is provided as follows: Modelling for water temperature using a single layer neural network (mlp() in tidymodels). Used relative humidity, precipitation flux and air temperature as drivers. Hypertuned parameters for models to be run with 100 epochs and penalty value of 0.01..\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/persistenceRW.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/persistenceRW.json index a00414e7b..eb8ed8199 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/persistenceRW.json @@ -14,30 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -102.4471, - 39.7582 - ], - [ - -82.0084, - 29.676 - ], - [ - -119.2575, - 37.0597 - ], - [ - -110.5871, - 44.9501 - ], - [ - -96.6242, - 34.4442 - ], - [ - -87.7982, - 32.5415 - ], [ -147.504, 65.1532 @@ -149,13 +125,37 @@ [ -105.9154, 39.8914 + ], + [ + -102.4471, + 39.7582 + ], + [ + -82.0084, + 29.676 + ], + [ + -119.2575, + 37.0597 + ], + [ + -110.5871, + 44.9501 + ], + [ + -96.6242, + 34.4442 + ], + [ + -87.7982, + 32.5415 ] ] }, "properties": { "title": "persistenceRW", - "description": "All scores for the Daily_Water_temperature variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ @@ -186,12 +186,6 @@ "temperature", "Daily", "P1D", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", - "BLWA", "CARI", "COMO", "CRAM", @@ -219,7 +213,13 @@ "TOMB", "TOOK", "WALK", - "WLOU" + "WLOU", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", + "BLWA" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/tg_arima.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/tg_arima.json index 00bcde2aa..35fe3cd03 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/tg_arima.json @@ -14,6 +14,14 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -84.2793, + 35.9574 + ], + [ + -105.9154, + 39.8914 + ], [ -102.4471, 39.7582 @@ -141,27 +149,19 @@ [ -149.6106, 68.6307 - ], - [ - -84.2793, - 35.9574 - ], - [ - -105.9154, - 39.8914 ] ] }, "properties": { "title": "tg_arima", - "description": "All scores for the Daily_Water_temperature variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Gregory Harrison", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", @@ -186,6 +186,8 @@ "temperature", "Daily", "P1D", + "WALK", + "WLOU", "ARIK", "BARC", "BIGC", @@ -217,9 +219,7 @@ "SYCA", "TECR", "TOMB", - "TOOK", - "WALK", - "WLOU" + "TOOK" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/tg_ets.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/tg_ets.json index 9012c585b..86d9d14eb 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/tg_ets.json @@ -14,46 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -99.1139, - 47.1591 - ], - [ - -99.2531, - 47.1298 - ], - [ - -111.7979, - 40.7839 - ], - [ - -82.0177, - 29.6878 - ], - [ - -111.5081, - 33.751 - ], - [ - -119.0274, - 36.9559 - ], - [ - -88.1589, - 31.8534 - ], - [ - -149.6106, - 68.6307 - ], - [ - -84.2793, - 35.9574 - ], - [ - -105.9154, - 39.8914 - ], [ -102.4471, 39.7582 @@ -149,13 +109,53 @@ [ -97.7823, 33.3785 + ], + [ + -99.1139, + 47.1591 + ], + [ + -99.2531, + 47.1298 + ], + [ + -111.7979, + 40.7839 + ], + [ + -82.0177, + 29.6878 + ], + [ + -111.5081, + 33.751 + ], + [ + -119.0274, + 36.9559 + ], + [ + -88.1589, + 31.8534 + ], + [ + -149.6106, + 68.6307 + ], + [ + -84.2793, + 35.9574 + ], + [ + -105.9154, + 39.8914 ] ] }, "properties": { "title": "tg_ets", - "description": "All scores for the Daily_Water_temperature variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -186,16 +186,6 @@ "temperature", "Daily", "P1D", - "PRLA", - "PRPO", - "REDB", - "SUGG", - "SYCA", - "TECR", - "TOMB", - "TOOK", - "WALK", - "WLOU", "ARIK", "BARC", "BIGC", @@ -219,7 +209,17 @@ "MCRA", "OKSR", "POSE", - "PRIN" + "PRIN", + "PRLA", + "PRPO", + "REDB", + "SUGG", + "SYCA", + "TECR", + "TOMB", + "TOOK", + "WALK", + "WLOU" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/tg_tbats.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/tg_tbats.json index ed6a04e65..7c04f20b5 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/tg_tbats.json @@ -14,70 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -102.4471, - 39.7582 - ], - [ - -82.0084, - 29.676 - ], - [ - -119.2575, - 37.0597 - ], - [ - -110.5871, - 44.9501 - ], - [ - -96.6242, - 34.4442 - ], - [ - -87.7982, - 32.5415 - ], - [ - -147.504, - 65.1532 - ], - [ - -105.5442, - 40.035 - ], - [ - -89.4737, - 46.2097 - ], - [ - -66.9868, - 18.1135 - ], - [ - -84.4374, - 31.1854 - ], - [ - -66.7987, - 18.1741 - ], - [ - -72.3295, - 42.4719 - ], - [ - -96.6038, - 39.1051 - ], - [ - -83.5038, - 35.6904 - ], - [ - -77.9832, - 39.0956 - ], [ -89.7048, 45.9983 @@ -149,13 +85,77 @@ [ -105.9154, 39.8914 + ], + [ + -102.4471, + 39.7582 + ], + [ + -82.0084, + 29.676 + ], + [ + -119.2575, + 37.0597 + ], + [ + -110.5871, + 44.9501 + ], + [ + -96.6242, + 34.4442 + ], + [ + -87.7982, + 32.5415 + ], + [ + -147.504, + 65.1532 + ], + [ + -105.5442, + 40.035 + ], + [ + -89.4737, + 46.2097 + ], + [ + -66.9868, + 18.1135 + ], + [ + -84.4374, + 31.1854 + ], + [ + -66.7987, + 18.1741 + ], + [ + -72.3295, + 42.4719 + ], + [ + -96.6038, + 39.1051 + ], + [ + -83.5038, + 35.6904 + ], + [ + -77.9832, + 39.0956 ] ] }, "properties": { "title": "tg_tbats", - "description": "All scores for the Daily_Water_temperature variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -186,22 +186,6 @@ "temperature", "Daily", "P1D", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", - "BLWA", - "CARI", - "COMO", - "CRAM", - "CUPE", - "FLNT", - "GUIL", - "HOPB", - "KING", - "LECO", - "LEWI", "LIRO", "MART", "MAYF", @@ -219,7 +203,23 @@ "TOMB", "TOOK", "WALK", - "WLOU" + "WLOU", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", + "BLWA", + "CARI", + "COMO", + "CRAM", + "CUPE", + "FLNT", + "GUIL", + "HOPB", + "KING", + "LECO", + "LEWI" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/zimmerman_proj1.json b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/zimmerman_proj1.json index e4ff7c146..e360133ae 100644 --- a/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/zimmerman_proj1.json +++ b/data/challenge/neon4cast-stac/scores/Aquatics/Daily_Water_temperature/models/zimmerman_proj1.json @@ -47,7 +47,7 @@ "properties": { "title": "zimmerman_proj1", "description": "All scores for the Daily_Water_temperature variable for the zimmerman_proj1 model. Information for the model is provided as follows: I used an ARIMA model with one autoregressive term. I also included air pressure and air temperature.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json b/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json index dde9ff334..776ef3d8e 100644 --- a/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json @@ -14,26 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], [ -97.57, 33.4012 @@ -201,19 +181,39 @@ [ -110.5391, 44.9535 + ], + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 ] ] }, "properties": { "title": "tg_arima", - "description": "All scores for the Weekly_beetle_community_abundance variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All scores for the Weekly_beetle_community_abundance variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-17T00:00:00Z", "end_datetime": "2024-07-22T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Gregory Harrison", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", @@ -238,11 +238,6 @@ "abundance", "Weekly", "P1W", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", "CLBJ", "CPER", "DCFS", @@ -284,7 +279,12 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json b/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json index 9a4df0153..f786ecb93 100644 --- a/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_ets", "description": "All scores for the Weekly_beetle_community_abundance variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-17T00:00:00Z", "end_datetime": "2024-07-22T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json b/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json index eb8b8f4b1..675fce48d 100644 --- a/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_tbats", "description": "All scores for the Weekly_beetle_community_abundance variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-17T00:00:00Z", "end_datetime": "2024-07-22T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_richness/collection.json b/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_richness/collection.json index 1ac355122..131f5a67a 100644 --- a/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_richness/collection.json +++ b/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_richness/collection.json @@ -11,17 +11,17 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/tg_ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/tg_tbats.json" }, { "rel": "parent", diff --git a/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_richness/models/tg_arima.json b/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_richness/models/tg_arima.json index 5d9fcee35..9b54dc99f 100644 --- a/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_richness/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_richness/models/tg_arima.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_arima", "description": "All scores for the Weekly_beetle_community_richness variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-17T00:00:00Z", "end_datetime": "2024-08-05T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Gregory Harrison", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_richness/models/tg_ets.json b/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_richness/models/tg_ets.json index 29fda2281..210fdaed0 100644 --- a/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_richness/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_richness/models/tg_ets.json @@ -14,42 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -87.3933, - 32.9505 - ], - [ - -119.006, - 37.0058 - ], - [ - -149.3705, - 68.6611 - ], - [ - -89.5857, - 45.4937 - ], - [ - -95.1921, - 39.0404 - ], - [ - -89.5373, - 46.2339 - ], - [ - -99.2413, - 47.1282 - ], - [ - -121.9519, - 45.8205 - ], - [ - -110.5391, - 44.9535 - ], [ -122.3303, 45.7624 @@ -201,13 +165,49 @@ [ -103.0293, 40.4619 + ], + [ + -87.3933, + 32.9505 + ], + [ + -119.006, + 37.0058 + ], + [ + -149.3705, + 68.6611 + ], + [ + -89.5857, + 45.4937 + ], + [ + -95.1921, + 39.0404 + ], + [ + -89.5373, + 46.2339 + ], + [ + -99.2413, + 47.1282 + ], + [ + -121.9519, + 45.8205 + ], + [ + -110.5391, + 44.9535 ] ] }, "properties": { "title": "tg_ets", - "description": "All scores for the Weekly_beetle_community_richness variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All scores for the Weekly_beetle_community_richness variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-17T00:00:00Z", "end_datetime": "2024-08-05T00:00:00Z", "providers": [ @@ -238,15 +238,6 @@ "richness", "Weekly", "P1W", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -284,7 +275,16 @@ "SOAP", "SRER", "STEI", - "STER" + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json b/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json index 722965d79..67517cf86 100644 --- a/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/scores/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_tbats", "description": "All scores for the Weekly_beetle_community_richness variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-17T00:00:00Z", "end_datetime": "2024-08-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/collection.json b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/collection.json index 34a526dfb..b9c7789df 100644 --- a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/collection.json +++ b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/collection.json @@ -11,7 +11,7 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", @@ -26,17 +26,17 @@ { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/ChlorophyllCrusaders.json" }, { "rel": "item", "type": "application/json", - "href": "./models/ChlorophyllCrusaders.json" + "href": "./models/climatology.json" }, { "rel": "parent", diff --git a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json index 274c6899a..247a04985 100644 --- a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json +++ b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json @@ -23,7 +23,7 @@ "properties": { "title": "ChlorophyllCrusaders", "description": "All scores for the Daily_Green_chromatic_coordinate variable for the ChlorophyllCrusaders model. Information for the model is provided as follows: Our project utilizes a historical GCC data to fit a Dynamic Linear Model (DLM). After this DLM is trained, we utilize forecasted temperature data to predict future GCC data..\n The model predicts this variable at the following sites: HEAL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-06-20T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json index 35ba5be97..3b7356c66 100644 --- a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json +++ b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json @@ -14,18 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -99.2413, - 47.1282 - ], - [ - -121.9519, - 45.8205 - ], - [ - -110.5391, - 44.9535 - ], [ -122.3303, 45.7624 @@ -201,13 +189,25 @@ [ -89.5373, 46.2339 + ], + [ + -99.2413, + 47.1282 + ], + [ + -121.9519, + 45.8205 + ], + [ + -110.5391, + 44.9535 ] ] }, "properties": { "title": "climatology", - "description": "All scores for the Daily_Green_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All scores for the Daily_Green_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-07T00:00:00Z", "providers": [ @@ -238,9 +238,6 @@ "gcc_90", "Daily", "P1D", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -284,7 +281,10 @@ "TOOL", "TREE", "UKFS", - "UNDE" + "UNDE", + "WOOD", + "WREF", + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json index 278f3d981..090e36023 100644 --- a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json @@ -207,7 +207,7 @@ "properties": { "title": "persistenceRW", "description": "All scores for the Daily_Green_chromatic_coordinate variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-07-28T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json index 9d1aa9064..925e71af3 100644 --- a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json @@ -14,62 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -119.7323, - 37.1088 - ], - [ - -119.2622, - 37.0334 - ], - [ - -110.8355, - 31.9107 - ], - [ - -89.5864, - 45.5089 - ], - [ - -103.0293, - 40.4619 - ], - [ - -87.3933, - 32.9505 - ], - [ - -119.006, - 37.0058 - ], - [ - -149.3705, - 68.6611 - ], - [ - -89.5857, - 45.4937 - ], - [ - -95.1921, - 39.0404 - ], - [ - -89.5373, - 46.2339 - ], - [ - -99.2413, - 47.1282 - ], - [ - -121.9519, - 45.8205 - ], - [ - -110.5391, - 44.9535 - ], [ -122.3303, 45.7624 @@ -201,19 +145,75 @@ [ -76.56, 38.8901 + ], + [ + -119.7323, + 37.1088 + ], + [ + -119.2622, + 37.0334 + ], + [ + -110.8355, + 31.9107 + ], + [ + -89.5864, + 45.5089 + ], + [ + -103.0293, + 40.4619 + ], + [ + -87.3933, + 32.9505 + ], + [ + -119.006, + 37.0058 + ], + [ + -149.3705, + 68.6611 + ], + [ + -89.5857, + 45.4937 + ], + [ + -95.1921, + 39.0404 + ], + [ + -89.5373, + 46.2339 + ], + [ + -99.2413, + 47.1282 + ], + [ + -121.9519, + 45.8205 + ], + [ + -110.5391, + 44.9535 ] ] }, "properties": { "title": "tg_arima", - "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-07-28T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Gregory Harrison", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", @@ -238,20 +238,6 @@ "gcc_90", "Daily", "P1D", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -284,7 +270,21 @@ "PUUM", "RMNP", "SCBI", - "SERC" + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json index 66dab66b7..b78371dfc 100644 --- a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json @@ -14,6 +14,26 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -95.1921, + 39.0404 + ], + [ + -89.5373, + 46.2339 + ], + [ + -99.2413, + 47.1282 + ], + [ + -121.9519, + 45.8205 + ], + [ + -110.5391, + 44.9535 + ], [ -122.3303, 45.7624 @@ -181,33 +201,13 @@ [ -89.5857, 45.4937 - ], - [ - -95.1921, - 39.0404 - ], - [ - -89.5373, - 46.2339 - ], - [ - -99.2413, - 47.1282 - ], - [ - -121.9519, - 45.8205 - ], - [ - -110.5391, - 44.9535 ] ] }, "properties": { "title": "tg_ets", - "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-07-28T00:00:00Z", "providers": [ @@ -238,6 +238,11 @@ "gcc_90", "Daily", "P1D", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -279,12 +284,7 @@ "TALL", "TEAK", "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "TREE" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json index 950317034..f45d8ed9f 100644 --- a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json @@ -14,6 +14,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], [ -147.5026, 65.154 @@ -185,29 +201,13 @@ [ -110.5391, 44.9535 - ], - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 ] ] }, "properties": { "title": "tg_tbats", - "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-07-28T00:00:00Z", "providers": [ @@ -238,6 +238,10 @@ "gcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", "BONA", "CLBJ", "CPER", @@ -280,11 +284,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN" + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/collection.json b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/collection.json index f251d3766..ae8477fc7 100644 --- a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/collection.json +++ b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/collection.json @@ -11,17 +11,17 @@ { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/baseline_ensemble.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/baseline_ensemble.json" + "href": "./models/tg_arima.json" }, { "rel": "item", @@ -36,7 +36,7 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/persistenceRW.json" }, { "rel": "parent", diff --git a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json index b0f9f1134..44b8e86bd 100644 --- a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json +++ b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json @@ -14,18 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], [ -78.0418, 39.0337 @@ -201,13 +189,25 @@ [ -110.5391, 44.9535 + ], + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 ] ] }, "properties": { "title": "baseline_ensemble", - "description": "All scores for the Daily_Red_chromatic_coordinate variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All scores for the Daily_Red_chromatic_coordinate variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-01T00:00:00Z", "providers": [ @@ -238,9 +238,6 @@ "rcc_90", "Daily", "P1D", - "ABBY", - "BARR", - "BART", "BLAN", "BONA", "CLBJ", @@ -284,7 +281,10 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json index b6e5d4f04..97698e4f8 100644 --- a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json +++ b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json @@ -14,98 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -100.9154, - 46.7697 - ], - [ - -99.0588, - 35.4106 - ], - [ - -112.4524, - 40.1776 - ], - [ - -84.2826, - 35.9641 - ], - [ - -81.9934, - 29.6893 - ], - [ - -155.3173, - 19.5531 - ], - [ - -105.546, - 40.2759 - ], - [ - -78.1395, - 38.8929 - ], - [ - -76.56, - 38.8901 - ], - [ - -119.7323, - 37.1088 - ], - [ - -119.2622, - 37.0334 - ], - [ - -110.8355, - 31.9107 - ], - [ - -89.5864, - 45.5089 - ], - [ - -103.0293, - 40.4619 - ], - [ - -87.3933, - 32.9505 - ], - [ - -119.006, - 37.0058 - ], - [ - -149.3705, - 68.6611 - ], - [ - -89.5857, - 45.4937 - ], - [ - -95.1921, - 39.0404 - ], - [ - -89.5373, - 46.2339 - ], - [ - -99.2413, - 47.1282 - ], - [ - -121.9519, - 45.8205 - ], - [ - -110.5391, - 44.9535 - ], [ -122.3303, 45.7624 @@ -201,13 +109,105 @@ [ -105.5824, 40.0543 + ], + [ + -100.9154, + 46.7697 + ], + [ + -99.0588, + 35.4106 + ], + [ + -112.4524, + 40.1776 + ], + [ + -84.2826, + 35.9641 + ], + [ + -81.9934, + 29.6893 + ], + [ + -155.3173, + 19.5531 + ], + [ + -105.546, + 40.2759 + ], + [ + -78.1395, + 38.8929 + ], + [ + -76.56, + 38.8901 + ], + [ + -119.7323, + 37.1088 + ], + [ + -119.2622, + 37.0334 + ], + [ + -110.8355, + 31.9107 + ], + [ + -89.5864, + 45.5089 + ], + [ + -103.0293, + 40.4619 + ], + [ + -87.3933, + 32.9505 + ], + [ + -119.006, + 37.0058 + ], + [ + -149.3705, + 68.6611 + ], + [ + -89.5857, + 45.4937 + ], + [ + -95.1921, + 39.0404 + ], + [ + -89.5373, + 46.2339 + ], + [ + -99.2413, + 47.1282 + ], + [ + -121.9519, + 45.8205 + ], + [ + -110.5391, + 44.9535 ] ] }, "properties": { "title": "climatology", - "description": "All scores for the Daily_Red_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All scores for the Daily_Red_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-07T00:00:00Z", "providers": [ @@ -238,29 +238,6 @@ "rcc_90", "Daily", "P1D", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -284,7 +261,30 @@ "LENO", "MLBS", "MOAB", - "NIWO" + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json index 9cf9485f0..eb44765c3 100644 --- a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json @@ -14,6 +14,62 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -119.7323, + 37.1088 + ], + [ + -119.2622, + 37.0334 + ], + [ + -110.8355, + 31.9107 + ], + [ + -89.5864, + 45.5089 + ], + [ + -103.0293, + 40.4619 + ], + [ + -87.3933, + 32.9505 + ], + [ + -119.006, + 37.0058 + ], + [ + -149.3705, + 68.6611 + ], + [ + -89.5857, + 45.4937 + ], + [ + -95.1921, + 39.0404 + ], + [ + -89.5373, + 46.2339 + ], + [ + -99.2413, + 47.1282 + ], + [ + -121.9519, + 45.8205 + ], + [ + -110.5391, + 44.9535 + ], [ -122.3303, 45.7624 @@ -145,69 +201,13 @@ [ -76.56, 38.8901 - ], - [ - -119.7323, - 37.1088 - ], - [ - -119.2622, - 37.0334 - ], - [ - -110.8355, - 31.9107 - ], - [ - -89.5864, - 45.5089 - ], - [ - -103.0293, - 40.4619 - ], - [ - -87.3933, - 32.9505 - ], - [ - -119.006, - 37.0058 - ], - [ - -149.3705, - 68.6611 - ], - [ - -89.5857, - 45.4937 - ], - [ - -95.1921, - 39.0404 - ], - [ - -89.5373, - 46.2339 - ], - [ - -99.2413, - 47.1282 - ], - [ - -121.9519, - 45.8205 - ], - [ - -110.5391, - 44.9535 ] ] }, "properties": { "title": "persistenceRW", - "description": "All scores for the Daily_Red_chromatic_coordinate variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All scores for the Daily_Red_chromatic_coordinate variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ @@ -238,6 +238,20 @@ "rcc_90", "Daily", "P1D", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -270,21 +284,7 @@ "PUUM", "RMNP", "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "SERC" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json index a3ed2bcc4..1e84f62b6 100644 --- a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_arima", "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Gregory Harrison", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json index b3d99f366..d7da53740 100644 --- a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json @@ -14,6 +14,94 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 + ], + [ + -84.4686, + 31.1948 + ], + [ + -106.8425, + 32.5907 + ], + [ + -96.6129, + 39.1104 + ], + [ + -96.5631, + 39.1008 + ], + [ + -67.0769, + 18.0213 + ], + [ + -88.1612, + 31.8539 + ], + [ + -80.5248, + 37.3783 + ], [ -109.3883, 38.2483 @@ -113,101 +201,13 @@ [ -110.5391, 44.9535 - ], - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 - ], - [ - -84.4686, - 31.1948 - ], - [ - -106.8425, - 32.5907 - ], - [ - -96.6129, - 39.1104 - ], - [ - -96.5631, - 39.1008 - ], - [ - -67.0769, - 18.0213 - ], - [ - -88.1612, - 31.8539 - ], - [ - -80.5248, - 37.3783 ] ] }, "properties": { "title": "tg_ets", - "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -238,6 +238,28 @@ "rcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", + "KONA", + "KONZ", + "LAJA", + "LENO", + "MLBS", "MOAB", "NIWO", "NOGP", @@ -262,29 +284,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA", - "KONZ", - "LAJA", - "LENO", - "MLBS" + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json index 76795e11d..4b4e5d0ad 100644 --- a/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/scores/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_tbats", "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/collection.json b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/collection.json index 4a9ec1a76..0d5fe3c56 100644 --- a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/collection.json +++ b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/collection.json @@ -11,32 +11,32 @@ { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/tg_ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/bookcast_forest.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/bookcast_forest.json" }, { "rel": "parent", diff --git a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json index 0f817c052..722eedca9 100644 --- a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json +++ b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json @@ -23,7 +23,7 @@ "properties": { "title": "bookcast_forest", "description": "All scores for the Daily_Net_ecosystem_exchange variable for the bookcast_forest model. Information for the model is provided as follows: A simple daily timestep process-based model of a terrestrial carbon cycle. It includes leaves, wood, and soil pools. It uses a light-use efficiency GPP model to convert PAR to carbon. The model is derived from https://github.com/mdietze/FluxCourseForecast..\n The model predicts this variable at the following sites: OSBS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-07-12T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/climatology.json b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/climatology.json index b973f63dc..ff4dded2a 100644 --- a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/climatology.json +++ b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/climatology.json @@ -207,7 +207,7 @@ "properties": { "title": "climatology", "description": "All scores for the Daily_Net_ecosystem_exchange variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-03T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json index be82f7e55..5aab054c4 100644 --- a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json @@ -14,6 +14,18 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -99.2413, + 47.1282 + ], + [ + -121.9519, + 45.8205 + ], + [ + -110.5391, + 44.9535 + ], [ -122.3303, 45.7624 @@ -189,25 +201,13 @@ [ -89.5373, 46.2339 - ], - [ - -99.2413, - 47.1282 - ], - [ - -121.9519, - 45.8205 - ], - [ - -110.5391, - 44.9535 ] ] }, "properties": { "title": "persistenceRW", - "description": "All scores for the Daily_Net_ecosystem_exchange variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All scores for the Daily_Net_ecosystem_exchange variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-03T00:00:00Z", "providers": [ @@ -238,6 +238,9 @@ "nee", "Daily", "P1D", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -281,10 +284,7 @@ "TOOL", "TREE", "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "UNDE" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json index 1a8ae5893..c280c8783 100644 --- a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json @@ -14,70 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 - ], - [ - -84.4686, - 31.1948 - ], [ -106.8425, 32.5907 @@ -201,19 +137,83 @@ [ -110.5391, 44.9535 + ], + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 + ], + [ + -84.4686, + 31.1948 ] ] }, "properties": { "title": "tg_arima", - "description": "All scores for the Daily_Net_ecosystem_exchange variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All scores for the Daily_Net_ecosystem_exchange variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Gregory Harrison", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", @@ -238,22 +238,6 @@ "nee", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", "JORN", "KONA", "KONZ", @@ -284,7 +268,23 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json index 75a619255..b17a4bc59 100644 --- a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_ets", "description": "All scores for the Daily_Net_ecosystem_exchange variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json index 86052f3e4..c81b8a062 100644 --- a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json @@ -14,6 +14,58 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -119.2622, + 37.0334 + ], + [ + -110.8355, + 31.9107 + ], + [ + -89.5864, + 45.5089 + ], + [ + -103.0293, + 40.4619 + ], + [ + -87.3933, + 32.9505 + ], + [ + -119.006, + 37.0058 + ], + [ + -149.3705, + 68.6611 + ], + [ + -89.5857, + 45.4937 + ], + [ + -95.1921, + 39.0404 + ], + [ + -89.5373, + 46.2339 + ], + [ + -99.2413, + 47.1282 + ], + [ + -121.9519, + 45.8205 + ], + [ + -110.5391, + 44.9535 + ], [ -122.3303, 45.7624 @@ -149,65 +201,13 @@ [ -119.7323, 37.1088 - ], - [ - -119.2622, - 37.0334 - ], - [ - -110.8355, - 31.9107 - ], - [ - -89.5864, - 45.5089 - ], - [ - -103.0293, - 40.4619 - ], - [ - -87.3933, - 32.9505 - ], - [ - -119.006, - 37.0058 - ], - [ - -149.3705, - 68.6611 - ], - [ - -89.5857, - 45.4937 - ], - [ - -95.1921, - 39.0404 - ], - [ - -89.5373, - 46.2339 - ], - [ - -99.2413, - 47.1282 - ], - [ - -121.9519, - 45.8205 - ], - [ - -110.5391, - 44.9535 ] ] }, "properties": { "title": "tg_tbats", - "description": "All scores for the Daily_Net_ecosystem_exchange variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All scores for the Daily_Net_ecosystem_exchange variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -238,6 +238,19 @@ "nee", "Daily", "P1D", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -271,20 +284,7 @@ "RMNP", "SCBI", "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "SJER" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/models/climatology.json b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/models/climatology.json index 2a9c92460..74560faf1 100644 --- a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/models/climatology.json +++ b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/models/climatology.json @@ -207,7 +207,7 @@ "properties": { "title": "climatology", "description": "All scores for the Daily_latent_heat_flux variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json index 981337b1e..a227317b5 100644 --- a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_arima", "description": "All scores for the Daily_latent_heat_flux variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Gregory Harrison", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json index d695b789b..a86323887 100644 --- a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json @@ -14,62 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], [ -149.2133, 63.8758 @@ -201,13 +145,69 @@ [ -110.5391, 44.9535 + ], + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 ] ] }, "properties": { "title": "tg_ets", - "description": "All scores for the Daily_latent_heat_flux variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All scores for the Daily_latent_heat_flux variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -238,20 +238,6 @@ "le", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", "HEAL", "JERC", "JORN", @@ -284,7 +270,21 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json index d70a709ec..8c9a551fe 100644 --- a/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/scores/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_tbats", "description": "All scores for the Daily_latent_heat_flux variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-06-13T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/sites/collection.json b/data/challenge/neon4cast-stac/sites/collection.json index 7def0d0d4..8fa4b0760 100644 --- a/data/challenge/neon4cast-stac/sites/collection.json +++ b/data/challenge/neon4cast-stac/sites/collection.json @@ -58,7 +58,7 @@ "interval": [ [ "2013-03-07T00:00:00Z", - "2024-09-04T00:00:00Z" + "2024-09-05T00:00:00Z" ] ] } diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/collection.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/collection.json index bb0fb9d9d..47f5900c2 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/collection.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/collection.json @@ -11,107 +11,107 @@ { "rel": "item", "type": "application/json", - "href": "./models/USGSHABs1.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/tg_lasso.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/procBlanchardMonod.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/procCTMIMonod.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", "type": "application/json", - "href": "./models/procEppleyNorbergMonod.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/procEppleyNorbergSteele.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/procHinshelwoodMonod.json" + "href": "./models/tg_temp_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/procHinshelwoodSteele.json" + "href": "./models/USGSHABs1.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/procBlanchardMonod.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_lasso.json" + "href": "./models/procCTMIMonod.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/procEppleyNorbergMonod.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/procEppleyNorbergSteele.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/procHinshelwoodMonod.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/procHinshelwoodSteele.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" + "href": "./models/tg_ets.json" }, { "rel": "parent", diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/USGSHABs1.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/USGSHABs1.json index 19c31ccb0..cb804cc9f 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/USGSHABs1.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/USGSHABs1.json @@ -31,7 +31,7 @@ "properties": { "title": "USGSHABs1", "description": "All summaries for the Daily_Chlorophyll_a variable for the USGSHABs1 model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BLWA, TOMB, FLNT.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-12T00:00:00Z", "end_datetime": "2024-03-09T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/cb_prophet.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/cb_prophet.json index 6344b61b5..e00dfb347 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/cb_prophet.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/cb_prophet.json @@ -55,7 +55,7 @@ "properties": { "title": "cb_prophet", "description": "All summaries for the Daily_Chlorophyll_a variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-10T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/climatology.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/climatology.json index 69a587913..9a792b6c8 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/climatology.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/climatology.json @@ -59,7 +59,7 @@ "properties": { "title": "climatology", "description": "All summaries for the Daily_Chlorophyll_a variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: BARC, BLWA, FLNT, SUGG, TOMB, CRAM, LIRO, PRPO, PRLA, TOOK, USGS-01427510, USGS-01463500, USGS-05543010, USGS-05553700, USGS-05558300, USGS-05586300, USGS-14181500, USGS-14211010, USGS-14211720.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-09-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json index fafbeb9f0..10974a02d 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/persistenceRW.json @@ -59,7 +59,7 @@ "properties": { "title": "persistenceRW", "description": "All summaries for the Daily_Chlorophyll_a variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, BARC, BLWA, CRAM, FLNT.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procBlanchardMonod.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procBlanchardMonod.json index 5842b9806..fd00fa47e 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procBlanchardMonod.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procBlanchardMonod.json @@ -47,7 +47,7 @@ "properties": { "title": "procBlanchardMonod", "description": "All summaries for the Daily_Chlorophyll_a variable for the procBlanchardMonod model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-13T00:00:00Z", "end_datetime": "2024-03-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procCTMIMonod.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procCTMIMonod.json index d1c5122ed..d3043e9c7 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procCTMIMonod.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procCTMIMonod.json @@ -47,7 +47,7 @@ "properties": { "title": "procCTMIMonod", "description": "All summaries for the Daily_Chlorophyll_a variable for the procCTMIMonod model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-13T00:00:00Z", "end_datetime": "2024-03-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergMonod.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergMonod.json index 6eaa079c7..ea459ed38 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergMonod.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergMonod.json @@ -47,13 +47,13 @@ "properties": { "title": "procEppleyNorbergMonod", "description": "All summaries for the Daily_Chlorophyll_a variable for the procEppleyNorbergMonod model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-13T00:00:00Z", "end_datetime": "2024-03-06T00:00:00Z", "providers": [ { - "url": "https://projects.ecoforecast.org/neon4cast-ci/", - "name": "NEON Ecological Forecasting Project", + "url": null, + "name": "Abigail Lewis", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergSteele.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergSteele.json index c3ddc1520..17a078be6 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergSteele.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procEppleyNorbergSteele.json @@ -47,7 +47,7 @@ "properties": { "title": "procEppleyNorbergSteele", "description": "All summaries for the Daily_Chlorophyll_a variable for the procEppleyNorbergSteele model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-13T00:00:00Z", "end_datetime": "2024-03-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodMonod.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodMonod.json index 33445b258..3a7c6d229 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodMonod.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodMonod.json @@ -47,13 +47,13 @@ "properties": { "title": "procHinshelwoodMonod", "description": "All summaries for the Daily_Chlorophyll_a variable for the procHinshelwoodMonod model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-13T00:00:00Z", "end_datetime": "2024-03-06T00:00:00Z", "providers": [ { - "url": null, - "name": "Abigail Lewis", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodSteele.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodSteele.json index 6f058e3eb..cbbd10948 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodSteele.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/procHinshelwoodSteele.json @@ -47,7 +47,7 @@ "properties": { "title": "procHinshelwoodSteele", "description": "All summaries for the Daily_Chlorophyll_a variable for the procHinshelwoodSteele model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-13T00:00:00Z", "end_datetime": "2024-03-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json index cdcf9b11d..fb15d045a 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_arima.json @@ -59,13 +59,13 @@ "properties": { "title": "tg_arima", "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Gregory Harrison", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json index 022994e1f..e90f1dc4f 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_ets.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_ets", "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm.json index e5515f80d..461619262 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_humidity_lm.json @@ -14,6 +14,10 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -149.6106, + 68.6307 + ], [ -82.0084, 29.676 @@ -49,23 +53,19 @@ [ -88.1589, 31.8534 - ], - [ - -149.6106, - 68.6307 ] ] }, "properties": { "title": "tg_humidity_lm", - "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: TOOK, BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ { - "url": "https://projects.ecoforecast.org/neon4cast-ci/", - "name": "NEON Ecological Forecasting Project", + "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", + "name": "Abigail Lewis", "roles": [ "producer", "processor", @@ -90,6 +90,7 @@ "chla", "Daily", "P1D", + "TOOK", "BARC", "BLWA", "CRAM", @@ -98,8 +99,7 @@ "PRLA", "PRPO", "SUGG", - "TOMB", - "TOOK" + "TOMB" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm.json index e5d8f4b9d..5946abd3d 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_precip_lm", "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm_all_sites.json index 4fb90a66f..2641e2d57 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_precip_lm_all_sites.json @@ -59,13 +59,13 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-19T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "name": "Gregory Harrison", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_randfor.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_randfor.json index aeac20253..38ce23b04 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_randfor.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_randfor", "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json index b3cf673a8..3095c2620 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_tbats.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_tbats", "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm.json index 4fc14d31b..62e31c7e3 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm.json @@ -59,7 +59,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm_all_sites.json index 9a5044507..7e49857b6 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Chlorophyll_a/models/tg_temp_lm_all_sites.json @@ -59,13 +59,13 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/collection.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/collection.json index 19a8eda40..3ce54a066 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/collection.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/collection.json @@ -11,77 +11,77 @@ { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/tg_ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/tg_lasso.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/GLEON_lm_lag_1day.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/air2waterSat_2.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_lasso.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/GLEON_lm_lag_1day.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/air2waterSat_2.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/tg_temp_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" + "href": "./models/climatology.json" }, { "rel": "item", diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json index 0b90b67bb..7e16c4f67 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/AquaticEcosystemsOxygen.json @@ -31,7 +31,7 @@ "properties": { "title": "AquaticEcosystemsOxygen", "description": "All summaries for the Daily_Dissolved_oxygen variable for the AquaticEcosystemsOxygen model. Information for the model is provided as follows: Used a Bayesian Dynamic Linear Model using the fit_dlm function from the ecoforecastR package.\n The model predicts this variable at the following sites: BARC, WLOU, ARIK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-04-03T00:00:00Z", "end_datetime": "2024-08-04T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/GLEON_lm_lag_1day.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/GLEON_lm_lag_1day.json index 3b45415e1..ad9f90cad 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/GLEON_lm_lag_1day.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/GLEON_lm_lag_1day.json @@ -47,7 +47,7 @@ "properties": { "title": "GLEON_lm_lag_1day", "description": "All summaries for the Daily_Dissolved_oxygen variable for the GLEON_lm_lag_1day model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-02-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/air2waterSat_2.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/air2waterSat_2.json index 313dd8e52..d2196882f 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/air2waterSat_2.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/air2waterSat_2.json @@ -155,13 +155,13 @@ "properties": { "title": "air2waterSat_2", "description": "All summaries for the Daily_Dissolved_oxygen variable for the air2waterSat_2 model. Information for the model is provided as follows: The air2water model is a linear model fit using the function lm() in R and uses air temperature as\na covariate.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, TOMB, TOOK, WALK, WLOU, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ { "url": "https://github.com/rqthomas/neon4cast-example/blob/main/forecast_model.R", - "name": "Quinn Thomas", + "name": "Freya Olsson", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/cb_prophet.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/cb_prophet.json index a9534c670..b409f8db3 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/cb_prophet.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/cb_prophet.json @@ -147,7 +147,7 @@ "properties": { "title": "cb_prophet", "description": "All summaries for the Daily_Dissolved_oxygen variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, ARIK, BARC, BIGC, BLDE.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-10T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/climatology.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/climatology.json index 45e226db4..58651e8ba 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/climatology.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/climatology.json @@ -15,48 +15,12 @@ "type": "MultiPoint", "coordinates": [ [ - -111.7979, - 40.7839 - ], - [ - -82.0177, - 29.6878 - ], - [ - -111.5081, - 33.751 - ], - [ - -119.0274, - 36.9559 - ], - [ - -84.2793, - 35.9574 - ], - [ - -105.9154, - 39.8914 - ], - [ - -102.4471, - 39.7582 - ], - [ - -82.0084, - 29.676 - ], - [ - -119.2575, - 37.0597 - ], - [ - -110.5871, - 44.9501 + -87.7982, + 32.5415 ], [ - -96.6242, - 34.4442 + -147.504, + 65.1532 ], [ -105.5442, @@ -115,20 +79,56 @@ 33.3785 ], [ - -88.1589, - 31.8534 + -111.7979, + 40.7839 ], [ - -87.7982, - 32.5415 + -82.0177, + 29.6878 ], [ - -89.4737, - 46.2097 + -111.5081, + 33.751 ], [ - -147.504, - 65.1532 + -119.0274, + 36.9559 + ], + [ + -84.2793, + 35.9574 + ], + [ + -105.9154, + 39.8914 + ], + [ + -102.4471, + 39.7582 + ], + [ + -82.0084, + 29.676 + ], + [ + -119.2575, + 37.0597 + ], + [ + -110.5871, + 44.9501 + ], + [ + -96.6242, + 34.4442 + ], + [ + -88.1589, + 31.8534 + ], + [ + -89.4737, + 46.2097 ], [ -89.7048, @@ -154,8 +154,8 @@ }, "properties": { "title": "climatology", - "description": "All summaries for the Daily_Dissolved_oxygen variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: REDB, SUGG, SYCA, TECR, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, TOMB, BLWA, CRAM, CARI, LIRO, PRPO, PRLA, TOOK, OKSR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All summaries for the Daily_Dissolved_oxygen variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: BLWA, CARI, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, TOMB, CRAM, LIRO, PRPO, PRLA, TOOK, OKSR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-09-26T00:00:00Z", "providers": [ @@ -186,17 +186,8 @@ "oxygen", "Daily", "P1D", - "REDB", - "SUGG", - "SYCA", - "TECR", - "WALK", - "WLOU", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", + "BLWA", + "CARI", "COMO", "CUPE", "FLNT", @@ -211,10 +202,19 @@ "MCRA", "POSE", "PRIN", + "REDB", + "SUGG", + "SYCA", + "TECR", + "WALK", + "WLOU", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", "TOMB", - "BLWA", "CRAM", - "CARI", "LIRO", "PRPO", "PRLA", diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json index 67c99cc67..cec19f95f 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/hotdeck.json @@ -75,7 +75,7 @@ "properties": { "title": "hotdeck", "description": "All summaries for the Daily_Dissolved_oxygen variable for the hotdeck model. Information for the model is provided as follows: Uses a hot deck approach: - Take the latest observation/forecast. - Past observations from around the same window of the season are collected. - Values close to the latest observation/forecast are collected. - One of these is randomly sampled. - Its \"tomorrow\" observation is used as the forecast. - Repeat until forecast at step h..\n The model predicts this variable at the following sites: BARC, SUGG, KING, SYCA, BLDE, BIGC, MCRA, REDB, CRAM, LIRO, PRIN, POSE, MAYF, LEWI.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-04-05T00:00:00Z", "end_datetime": "2024-09-21T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json index d647412f2..ab7ef9a61 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/persistenceRW.json @@ -55,44 +55,52 @@ 33.751 ], [ - -102.4471, - 39.7582 + -87.4077, + 32.9604 ], [ - -82.0084, - 29.676 + -96.443, + 38.9459 ], [ - -119.2575, - 37.0597 + -122.1655, + 44.2596 ], [ - -110.5871, - 44.9501 + -149.143, + 68.6698 + ], + [ + -78.1473, + 38.8943 ], [ -96.6242, 34.4442 ], [ - -83.5038, - 35.6904 + -87.7982, + 32.5415 ], [ - -77.9832, - 39.0956 + -105.9154, + 39.8914 ], [ - -89.7048, - 45.9983 + -102.4471, + 39.7582 ], [ - -121.9338, - 45.7908 + -82.0084, + 29.676 ], [ - -87.4077, - 32.9604 + -119.2575, + 37.0597 + ], + [ + -110.5871, + 44.9501 ], [ -119.0274, @@ -110,14 +118,6 @@ -84.2793, 35.9574 ], - [ - -87.7982, - 32.5415 - ], - [ - -105.9154, - 39.8914 - ], [ -84.4374, 31.1854 @@ -135,27 +135,27 @@ 39.1051 ], [ - -96.443, - 38.9459 + -83.5038, + 35.6904 ], [ - -122.1655, - 44.2596 + -77.9832, + 39.0956 ], [ - -149.143, - 68.6698 + -89.7048, + 45.9983 ], [ - -78.1473, - 38.8943 + -121.9338, + 45.7908 ] ] }, "properties": { "title": "persistenceRW", - "description": "All summaries for the Daily_Dissolved_oxygen variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: CARI, COMO, CRAM, CUPE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, ARIK, BARC, BIGC, BLDE, BLUE, LECO, LEWI, LIRO, MART, MAYF, TECR, TOMB, TOOK, WALK, BLWA, WLOU, FLNT, GUIL, HOPB, KING, MCDI, MCRA, OKSR, POSE.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All summaries for the Daily_Dissolved_oxygen variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: CARI, COMO, CRAM, CUPE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, MAYF, MCDI, MCRA, OKSR, POSE, BLUE, BLWA, WLOU, ARIK, BARC, BIGC, BLDE, TECR, TOMB, TOOK, WALK, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ @@ -196,30 +196,30 @@ "REDB", "SUGG", "SYCA", + "MAYF", + "MCDI", + "MCRA", + "OKSR", + "POSE", + "BLUE", + "BLWA", + "WLOU", "ARIK", "BARC", "BIGC", "BLDE", - "BLUE", - "LECO", - "LEWI", - "LIRO", - "MART", - "MAYF", "TECR", "TOMB", "TOOK", "WALK", - "BLWA", - "WLOU", "FLNT", "GUIL", "HOPB", "KING", - "MCDI", - "MCRA", - "OKSR", - "POSE" + "LECO", + "LEWI", + "LIRO", + "MART" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json index c8dba248e..c35ab2f56 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_arima.json @@ -155,13 +155,13 @@ "properties": { "title": "tg_arima", "description": "All summaries for the Daily_Dissolved_oxygen variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Gregory Harrison", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json index d54d174ba..387aabc37 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_ets.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_ets", "description": "All summaries for the Daily_Dissolved_oxygen variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm.json index 6b948f7d3..b61f0cb1b 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm.json @@ -155,13 +155,13 @@ "properties": { "title": "tg_humidity_lm", "description": "All summaries for the Daily_Dissolved_oxygen variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ { - "url": "https://projects.ecoforecast.org/neon4cast-ci/", - "name": "NEON Ecological Forecasting Project", + "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", + "name": "Abigail Lewis", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm_all_sites.json index e5bddab8c..437b517ab 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_humidity_lm_all_sites.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All summaries for the Daily_Dissolved_oxygen variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_lasso.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_lasso.json index 7a3ab3f94..94eebc34a 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_lasso.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_lasso.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_lasso", "description": "All summaries for the Daily_Dissolved_oxygen variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm.json index 282a790a6..c18a7876a 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_precip_lm", "description": "All summaries for the Daily_Dissolved_oxygen variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm_all_sites.json index c664df626..68eec4146 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_precip_lm_all_sites.json @@ -155,13 +155,13 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All summaries for the Daily_Dissolved_oxygen variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-19T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "name": "Gregory Harrison", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_randfor.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_randfor.json index 9bbb1bdfb..ed9f1f43c 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_randfor.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_randfor", "description": "All summaries for the Daily_Dissolved_oxygen variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json index d6cd41cf3..ddd9c5ffe 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_tbats.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_tbats", "description": "All summaries for the Daily_Dissolved_oxygen variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm.json index 705285870..623e4bab0 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries for the Daily_Dissolved_oxygen variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm_all_sites.json index e8916466b..ea2495fef 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Dissolved_oxygen/models/tg_temp_lm_all_sites.json @@ -155,13 +155,13 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries for the Daily_Dissolved_oxygen variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/collection.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/collection.json index 15aa28ccf..b62ef86fb 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/collection.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/collection.json @@ -21,57 +21,62 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/fTSLM_lag.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/flareGLM.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_lasso.json" + "href": "./models/flareGLM_noDA.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fTSLM_lag.json" + "href": "./models/flareGOTM_noDA.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flareGLM.json" + "href": "./models/flareSimstrat_noDA.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flareGLM_noDA.json" + "href": "./models/flare_ler.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flareGOTM_noDA.json" + "href": "./models/flare_ler_baselines.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flareSimstrat_noDA.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flare_ler.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flare_ler_baselines.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/tg_lasso.json" + }, + { + "rel": "item", + "type": "application/json", + "href": "./models/climatology.json" }, { "rel": "item", @@ -106,27 +111,22 @@ { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/fARIMA_clim_ensemble.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fARIMA_clim_ensemble.json" + "href": "./models/GLEON_JRabaey_temp_physics.json" }, { "rel": "item", "type": "application/json", - "href": "./models/GLEON_JRabaey_temp_physics.json" + "href": "./models/GLEON_lm_lag_1day.json" }, { "rel": "item", "type": "application/json", - "href": "./models/GLEON_lm_lag_1day.json" + "href": "./models/GLEON_physics.json" }, { "rel": "item", @@ -141,7 +141,7 @@ { "rel": "item", "type": "application/json", - "href": "./models/GLEON_physics.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json index 62c3f6584..3165ec933 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/GLEON_JRabaey_temp_physics.json @@ -155,7 +155,7 @@ "properties": { "title": "GLEON_JRabaey_temp_physics", "description": "All summaries for the Daily_Water_temperature variable for the GLEON_JRabaey_temp_physics model. Information for the model is provided as follows: The JR-physics model is a simple process model based on the assumption that surface water\ntemperature should trend towards equilibration with air temperature with a lag factor..\n The model predicts this variable at the following sites: WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-12T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json index 6506182aa..2e270c986 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/GLEON_lm_lag_1day.json @@ -47,7 +47,7 @@ "properties": { "title": "GLEON_lm_lag_1day", "description": "All summaries for the Daily_Water_temperature variable for the GLEON_lm_lag_1day model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-02-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/GLEON_physics.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/GLEON_physics.json index 802bc53b6..45febffef 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/GLEON_physics.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/GLEON_physics.json @@ -43,7 +43,7 @@ "properties": { "title": "GLEON_physics", "description": "All summaries for the Daily_Water_temperature variable for the GLEON_physics model. Information for the model is provided as follows: A simple, process-based model was developed to replicate the water temperature dynamics of a\nsurface water layer sensu Chapra (2008). The model focus was only on quantifying the impacts of\natmosphere-water heat flux exchanges on the idealized near-surface water temperature dynamics.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2023-12-22T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/air2waterSat_2.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/air2waterSat_2.json index f2d856878..cb8ffd2be 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/air2waterSat_2.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/air2waterSat_2.json @@ -15,16 +15,8 @@ "type": "MultiPoint", "coordinates": [ [ - -149.6106, - 68.6307 - ], - [ - -84.2793, - 35.9574 - ], - [ - -105.9154, - 39.8914 + -77.9832, + 39.0956 ], [ -89.7048, @@ -147,21 +139,29 @@ 35.6904 ], [ - -77.9832, - 39.0956 + -149.6106, + 68.6307 + ], + [ + -84.2793, + 35.9574 + ], + [ + -105.9154, + 39.8914 ] ] }, "properties": { "title": "air2waterSat_2", - "description": "All summaries for the Daily_Water_temperature variable for the air2waterSat_2 model. Information for the model is provided as follows: The air2water model is a linear model fit using the function lm() in R and uses air temperature as\na covariate.\n The model predicts this variable at the following sites: TOOK, WALK, WLOU, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All summaries for the Daily_Water_temperature variable for the air2waterSat_2 model. Information for the model is provided as follows: The air2water model is a linear model fit using the function lm() in R and uses air temperature as\na covariate.\n The model predicts this variable at the following sites: LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, TOOK, WALK, WLOU.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ { "url": "https://github.com/rqthomas/neon4cast-example/blob/main/forecast_model.R", - "name": "Quinn Thomas", + "name": "Freya Olsson", "roles": [ "producer", "processor", @@ -186,9 +186,7 @@ "temperature", "Daily", "P1D", - "TOOK", - "WALK", - "WLOU", + "LEWI", "LIRO", "MART", "MAYF", @@ -219,7 +217,9 @@ "HOPB", "KING", "LECO", - "LEWI" + "TOOK", + "WALK", + "WLOU" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json index cdb73dfac..b278e652a 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/baseline_ensemble.json @@ -15,52 +15,32 @@ "type": "MultiPoint", "coordinates": [ [ - -87.7982, - 32.5415 - ], - [ - -105.5442, - 40.035 + -96.443, + 38.9459 ], [ - -66.9868, - 18.1135 + -122.1655, + 44.2596 ], [ - -84.4374, - 31.1854 + -78.1473, + 38.8943 ], [ - -66.7987, - 18.1741 + -97.7823, + 33.3785 ], [ - -72.3295, - 42.4719 + -111.7979, + 40.7839 ], [ -82.0177, 29.6878 ], [ - -111.5081, - 33.751 - ], - [ - -119.0274, - 36.9559 - ], - [ - -88.1589, - 31.8534 - ], - [ - -84.2793, - 35.9574 - ], - [ - -105.9154, - 39.8914 + -72.3295, + 42.4719 ], [ -96.6038, @@ -83,24 +63,44 @@ 32.9604 ], [ - -96.443, - 38.9459 + -111.5081, + 33.751 ], [ - -122.1655, - 44.2596 + -119.0274, + 36.9559 ], [ - -78.1473, - 38.8943 + -88.1589, + 31.8534 ], [ - -97.7823, - 33.3785 + -84.2793, + 35.9574 ], [ - -111.7979, - 40.7839 + -105.9154, + 39.8914 + ], + [ + -87.7982, + 32.5415 + ], + [ + -105.5442, + 40.035 + ], + [ + -66.9868, + 18.1135 + ], + [ + -84.4374, + 31.1854 + ], + [ + -66.7987, + 18.1741 ], [ -102.4471, @@ -154,8 +154,8 @@ }, "properties": { "title": "baseline_ensemble", - "description": "All summaries for the Daily_Water_temperature variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: BLWA, COMO, CUPE, FLNT, GUIL, HOPB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, ARIK, BARC, BIGC, BLDE, BLUE, CRAM, LIRO, PRLA, PRPO, CARI, OKSR, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All summaries for the Daily_Water_temperature variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: MCDI, MCRA, POSE, PRIN, REDB, SUGG, HOPB, KING, LECO, LEWI, MART, MAYF, SYCA, TECR, TOMB, WALK, WLOU, BLWA, COMO, CUPE, FLNT, GUIL, ARIK, BARC, BIGC, BLDE, BLUE, CRAM, LIRO, PRLA, PRPO, CARI, OKSR, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -186,28 +186,28 @@ "temperature", "Daily", "P1D", - "BLWA", - "COMO", - "CUPE", - "FLNT", - "GUIL", - "HOPB", + "MCDI", + "MCRA", + "POSE", + "PRIN", + "REDB", "SUGG", - "SYCA", - "TECR", - "TOMB", - "WALK", - "WLOU", + "HOPB", "KING", "LECO", "LEWI", "MART", "MAYF", - "MCDI", - "MCRA", - "POSE", - "PRIN", - "REDB", + "SYCA", + "TECR", + "TOMB", + "WALK", + "WLOU", + "BLWA", + "COMO", + "CUPE", + "FLNT", + "GUIL", "ARIK", "BARC", "BIGC", diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/cb_prophet.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/cb_prophet.json index e390ea651..9bfae4c96 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/cb_prophet.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/cb_prophet.json @@ -147,7 +147,7 @@ "properties": { "title": "cb_prophet", "description": "All summaries for the Daily_Water_temperature variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, POSE, PRIN, PRLA, PRPO, REDB, SUGG, TECR, TOMB, WALK, WLOU, SYCA.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-10T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/climatology.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/climatology.json index 98b586d0f..9c3d94f36 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/climatology.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/climatology.json @@ -14,14 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -102.4471, - 39.7582 - ], - [ - -82.0084, - 29.676 - ], [ -119.2575, 37.0597 @@ -38,10 +30,18 @@ -87.7982, 32.5415 ], + [ + -147.504, + 65.1532 + ], [ -105.5442, 40.035 ], + [ + -89.4737, + 46.2097 + ], [ -66.9868, 18.1135 @@ -70,6 +70,10 @@ -77.9832, 39.0956 ], + [ + -89.7048, + 45.9983 + ], [ -121.9338, 45.7908 @@ -94,6 +98,14 @@ -97.7823, 33.3785 ], + [ + -99.1139, + 47.1591 + ], + [ + -99.2531, + 47.1298 + ], [ -111.7979, 40.7839 @@ -110,37 +122,25 @@ -119.0274, 36.9559 ], - [ - -84.2793, - 35.9574 - ], - [ - -105.9154, - 39.8914 - ], [ -88.1589, 31.8534 ], [ - -89.7048, - 45.9983 - ], - [ - -99.2531, - 47.1298 + -84.2793, + 35.9574 ], [ - -89.4737, - 46.2097 + -105.9154, + 39.8914 ], [ - -99.1139, - 47.1591 + -102.4471, + 39.7582 ], [ - -147.504, - 65.1532 + -82.0084, + 29.676 ], [ -149.143, @@ -154,8 +154,8 @@ }, "properties": { "title": "climatology", - "description": "All summaries for the Daily_Water_temperature variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, WALK, WLOU, TOMB, LIRO, PRPO, CRAM, PRLA, CARI, OKSR, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All summaries for the Daily_Water_temperature variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, ARIK, BARC, OKSR, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-09-26T00:00:00Z", "providers": [ @@ -186,13 +186,13 @@ "temperature", "Daily", "P1D", - "ARIK", - "BARC", "BIGC", "BLDE", "BLUE", "BLWA", + "CARI", "COMO", + "CRAM", "CUPE", "FLNT", "GUIL", @@ -200,24 +200,24 @@ "KING", "LECO", "LEWI", + "LIRO", "MART", "MAYF", "MCDI", "MCRA", "POSE", "PRIN", + "PRLA", + "PRPO", "REDB", "SUGG", "SYCA", "TECR", + "TOMB", "WALK", "WLOU", - "TOMB", - "LIRO", - "PRPO", - "CRAM", - "PRLA", - "CARI", + "ARIK", + "BARC", "OKSR", "TOOK" ], diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json index dcad83901..9598b2582 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/fARIMA_clim_ensemble.json @@ -155,7 +155,7 @@ "properties": { "title": "fARIMA_clim_ensemble", "description": "All summaries for the Daily_Water_temperature variable for the fARIMA_clim_ensemble model. Information for the model is provided as follows: The fAMIRA-DOY MME is a multi-model ensemble (MME) composed of two empirical\nmodels: an ARIMA model (fARIMA) and day-of-year model.\n The model predicts this variable at the following sites: LECO, LEWI, MART, MAYF, MCDI, MCRA, COMO, CUPE, GUIL, HOPB, KING, ARIK, BARC, BLUE, BLWA, WALK, WLOU, POSE, PRIN, REDB, SUGG, TECR, TOMB, BIGC, BLDE, CRAM, FLNT, SYCA, LIRO, PRLA, PRPO, CARI, OKSR, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-10T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json index 4e69e2915..acc068a79 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/fTSLM_lag.json @@ -155,7 +155,7 @@ "properties": { "title": "fTSLM_lag", "description": "All summaries for the Daily_Water_temperature variable for the fTSLM_lag model. Information for the model is provided as follows: This is a simple time series linear model in which water temperature is a function of air\ntemperature of that day and the previous day\u2019s air temperature.\n The model predicts this variable at the following sites: TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-08T00:00:00Z", "end_datetime": "2024-09-14T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flareGLM.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flareGLM.json index 45f0ee781..3a00c303e 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flareGLM.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flareGLM.json @@ -47,7 +47,7 @@ "properties": { "title": "flareGLM", "description": "All summaries for the Daily_Water_temperature variable for the flareGLM model. Information for the model is provided as follows: The FLARE-GLM is a forecasting framework that integrates the General Lake Model\nhydrodynamic process model (GLM; Hipsey et al., 2019) and data assimilation algorithm to generate\nensemble forecasts of lake water temperature..\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json index 11902197d..e42a0898f 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flareGLM_noDA.json @@ -47,13 +47,13 @@ "properties": { "title": "flareGLM_noDA", "description": "All summaries for the Daily_Water_temperature variable for the flareGLM_noDA model. Information for the model is provided as follows: The FLARE-GLM is a forecasting framework that integrates the General Lake Model\nhydrodynamic process model (GLM; Hipsey et al., 2019). This version does not incorportate data assimilation.\n The model predicts this variable at the following sites: TOOK, BARC, CRAM, LIRO, PRLA, PRPO, SUGG.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-03-02T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ { "url": "https://github.com/FLARE-forecast/NEON-forecast-code/workflows/default", - "name": "Freya Olsson", + "name": "Joseph Rabaey", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flareGOTM_noDA.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flareGOTM_noDA.json index f74052117..04c4491f9 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flareGOTM_noDA.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flareGOTM_noDA.json @@ -47,13 +47,13 @@ "properties": { "title": "flareGOTM_noDA", "description": "All summaries for the Daily_Water_temperature variable for the flareGOTM_noDA model. Information for the model is provided as follows: FLARE-GOTM uses the General Ocean Turbulence Model (GOTM) hydrodynamic model. GOTM is a 1-D\nhydrodynamic turbulence model (Umlauf et al., 2005) that estimates water column temperatures.\n The model predicts this variable at the following sites: BARC, CRAM, SUGG, LIRO, PRLA, PRPO, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-03-08T00:00:00Z", "end_datetime": "2024-03-20T00:00:00Z", "providers": [ { "url": "https://github.com/FLARE-forecast/NEON-forecast-code/workflows/ler", - "name": "Joseph Rabaey", + "name": "Quinn Thomas", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flareSimstrat_noDA.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flareSimstrat_noDA.json index 61dc2b823..5a2fb06c3 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flareSimstrat_noDA.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flareSimstrat_noDA.json @@ -43,13 +43,13 @@ "properties": { "title": "flareSimstrat_noDA", "description": "All summaries for the Daily_Water_temperature variable for the flareSimstrat_noDA model. Information for the model is provided as follows: FLARE-Simstrat uses the same principles and overarching framework as FLARE-GLM with the\nhydrodynamic model replaced with Simstrat. Simstrat is a 1-D hydrodynamic turbulence model\n(Goudsmit et al., 2002) that estimates water column temperatures..\n The model predicts this variable at the following sites: BARC, SUGG, TOOK, CRAM, PRLA, PRPO.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-03-08T00:00:00Z", "end_datetime": "2024-03-19T00:00:00Z", "providers": [ { - "url": "https://github.com/FLARE-forecast/NEON-forecast-code/workflows/ler", - "name": "Quinn Thomas", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flare_ler.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flare_ler.json index 1d37470a3..bf08fc220 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flare_ler.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flare_ler.json @@ -43,7 +43,7 @@ "properties": { "title": "flare_ler", "description": "All summaries for the Daily_Water_temperature variable for the flare_ler model. Information for the model is provided as follows: The LER MME is a multi-model ensemble (MME) derived from the three process models from\nFLARE (FLARE-GLM, FLARE-GOTM, and FLARE-Simstrat). To generate the MME, an ensemble\nforecast was generated by sampling from the submitted models\u2019 ensemble members.\n The model predicts this variable at the following sites: SUGG, CRAM, LIRO, PRLA, PRPO, BARC.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-19T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flare_ler_baselines.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flare_ler_baselines.json index 24fe88b2e..db0e9fc86 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flare_ler_baselines.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/flare_ler_baselines.json @@ -27,7 +27,7 @@ "properties": { "title": "flare_ler_baselines", "description": "All summaries for the Daily_Water_temperature variable for the flare_ler_baselines model. Information for the model is provided as follows: The LER-baselines model is a multi-model ensemble (MME) comprised of the three process\nmodels from FLARE (FLARE-GLM, FLARE-GOTM, and FLARE-Simstrat) and the two baseline\nmodels (day-of-year, persistence), submitted by Challenge organisers. To generate the MME, an\nensemble forecast was generated by sampling from the submitted model\u2019s ensemble members (either\nfrom an ensemble forecast in the case of the FLARE models and persistence, or from the distribution for\nthe day-of-year forecasts).\n The model predicts this variable at the following sites: SUGG, BARC.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-19T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/hotdeck.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/hotdeck.json index 1954ca073..7fc04a8a3 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/hotdeck.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/hotdeck.json @@ -139,7 +139,7 @@ "properties": { "title": "hotdeck", "description": "All summaries for the Daily_Water_temperature variable for the hotdeck model. Information for the model is provided as follows: Uses a hot deck approach: - Take the latest observation/forecast. - Past observations from around the same window of the season are collected. - Values close to the latest observation/forecast are collected. - One of these is randomly sampled. - Its \"tomorrow\" observation is used as the forecast. - Repeat until forecast at step h..\n The model predicts this variable at the following sites: BARC, SUGG, TOMB, BLWA, FLNT, MCRA, KING, SYCA, POSE, PRIN, MAYF, LEWI, LECO, ARIK, HOPB, REDB, TECR, BLDE, COMO, WLOU, CRAM, CARI, BIGC, BLUE, CUPE, GUIL, WALK, LIRO, PRLA, PRPO.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-28T00:00:00Z", "end_datetime": "2024-09-21T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/persistenceRW.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/persistenceRW.json index a9880e976..cf6d729b7 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/persistenceRW.json @@ -155,7 +155,7 @@ "properties": { "title": "persistenceRW", "description": "All summaries for the Daily_Water_temperature variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: KING, LECO, LEWI, LIRO, MART, MAYF, ARIK, BARC, BIGC, BLDE, BLUE, MCDI, MCRA, OKSR, POSE, PRIN, WLOU, CUPE, FLNT, GUIL, HOPB, PRLA, PRPO, REDB, SUGG, SYCA, BLWA, CARI, COMO, CRAM, TECR, TOMB, TOOK, WALK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-09-25T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/precip_mod.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/precip_mod.json index a968359a1..346ccef73 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/precip_mod.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/precip_mod.json @@ -47,7 +47,7 @@ "properties": { "title": "precip_mod", "description": "All summaries for the Daily_Water_temperature variable for the precip_mod model. Information for the model is provided as follows: NA.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-12-21T00:00:00Z", "end_datetime": "2024-01-24T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_arima.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_arima.json index 360b56d13..c987097a6 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_arima.json @@ -155,13 +155,13 @@ "properties": { "title": "tg_arima", "description": "All summaries for the Daily_Water_temperature variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Gregory Harrison", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_ets.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_ets.json index e4d887362..d35b33ca9 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_ets.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_ets", "description": "All summaries for the Daily_Water_temperature variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_humidity_lm.json index 6aaf9756b..a8f8fa1aa 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_humidity_lm.json @@ -155,13 +155,13 @@ "properties": { "title": "tg_humidity_lm", "description": "All summaries for the Daily_Water_temperature variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ { - "url": "https://projects.ecoforecast.org/neon4cast-ci/", - "name": "NEON Ecological Forecasting Project", + "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", + "name": "Abigail Lewis", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_humidity_lm_all_sites.json index 062e46787..56cab011b 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_humidity_lm_all_sites.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All summaries for the Daily_Water_temperature variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_lasso.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_lasso.json index 8069242c9..c6d739fc1 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_lasso.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_lasso.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_lasso", "description": "All summaries for the Daily_Water_temperature variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_precip_lm.json index c273dbf65..3b8b73d67 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_precip_lm.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_precip_lm", "description": "All summaries for the Daily_Water_temperature variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_precip_lm_all_sites.json index a88fa793a..0c6884fbf 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_precip_lm_all_sites.json @@ -155,13 +155,13 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All summaries for the Daily_Water_temperature variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-19T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "name": "Gregory Harrison", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_randfor.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_randfor.json index fe7992604..2994df70c 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_randfor.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_randfor", "description": "All summaries for the Daily_Water_temperature variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-09-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_tbats.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_tbats.json index 75cfb526c..541da8a30 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_tbats.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_tbats", "description": "All summaries for the Daily_Water_temperature variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-18T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_temp_lm.json index 22887ca95..7767cc7ae 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_temp_lm.json @@ -155,7 +155,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries for the Daily_Water_temperature variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_temp_lm_all_sites.json index b7816a938..0011472d6 100644 --- a/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Aquatics/Daily_Water_temperature/models/tg_temp_lm_all_sites.json @@ -155,13 +155,13 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries for the Daily_Water_temperature variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/collection.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/collection.json index ecc52e127..ac0851a74 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/collection.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/collection.json @@ -56,12 +56,12 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "parent", diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json index a6d1c0881..7bfba61a5 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_arima.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_arima", "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-02-13T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Gregory Harrison", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json index adac3c257..360457455 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_ets.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_ets", "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-02-06T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm.json index 0f69193b2..b061ae542 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_humidity_lm", "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ { - "url": "https://projects.ecoforecast.org/neon4cast-ci/", - "name": "NEON Ecological Forecasting Project", + "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", + "name": "Abigail Lewis", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm_all_sites.json index 4b2b8ad9c..3b62b7f54 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_humidity_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_lasso.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_lasso.json index 0081fe87c..bc6863312 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_lasso.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_lasso.json @@ -199,7 +199,7 @@ "properties": { "title": "tg_lasso", "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: ABBY, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm.json index b8dd4ae15..eaf4108fa 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_precip_lm", "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm_all_sites.json index d56db38da..5ad8d2c6e 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_precip_lm_all_sites.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "name": "Gregory Harrison", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_randfor.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_randfor.json index b0217aa04..e3fae55de 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_randfor.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_randfor", "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-19T00:00:00Z", "end_datetime": "2024-03-01T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json index 32ab7cc43..f68f3a29c 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_tbats.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_tbats", "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm.json index ccfa5d309..4a078d847 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm_all_sites.json index 5105defa3..7c68a6680 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_abundance/models/tg_temp_lm_all_sites.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries for the Weekly_beetle_community_abundance variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_arima.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_arima.json index 8fe0e6f39..39a6396b8 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_arima.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_arima", "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-02-13T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Gregory Harrison", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_ets.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_ets.json index bfd71f93d..3940ffd67 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_ets.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_ets", "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-02-06T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm.json index 215ce9650..dfd416063 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_humidity_lm", "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ { - "url": "https://projects.ecoforecast.org/neon4cast-ci/", - "name": "NEON Ecological Forecasting Project", + "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", + "name": "Abigail Lewis", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm_all_sites.json index 169f99b90..a6efe1f98 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_humidity_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_lasso.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_lasso.json index aaebc5755..fb061b354 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_lasso.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_lasso.json @@ -199,7 +199,7 @@ "properties": { "title": "tg_lasso", "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: ABBY, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm.json index a5fd536b7..a52ce562f 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_precip_lm", "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm_all_sites.json index 6f428874b..b16833838 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_precip_lm_all_sites.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "name": "Gregory Harrison", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_randfor.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_randfor.json index a1b003ef0..895c34333 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_randfor.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_randfor", "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-19T00:00:00Z", "end_datetime": "2024-03-01T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json index 911eb1a6f..8229d6005 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_tbats.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_tbats", "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2025-07-07T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm.json index aab085160..f383a8354 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm_all_sites.json index c52e8b0e3..2e7bab0b8 100644 --- a/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Beetles/Weekly_beetle_community_richness/models/tg_temp_lm_all_sites.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries for the Weekly_beetle_community_richness variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/collection.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/collection.json index a8976ec6d..ebc21c5e0 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/collection.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/collection.json @@ -11,72 +11,72 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_humidity_lm_all_sites.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_lasso.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/tg_tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/tg_temp_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_temp_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/tg_ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_temp_lm_all_sites.json" + "href": "./models/tg_humidity_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/tg_humidity_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/tg_lasso.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json index 05774ab43..935e0275e 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/ChlorophyllCrusaders.json @@ -27,7 +27,7 @@ "properties": { "title": "ChlorophyllCrusaders", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the ChlorophyllCrusaders model. Information for the model is provided as follows: Our project utilizes a historical GCC data to fit a Dynamic Linear Model (DLM). After this DLM is trained, we utilize forecasted temperature data to predict future GCC data..\n The model predicts this variable at the following sites: HARV, HEAL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", "end_datetime": "2024-06-20T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/PEG.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/PEG.json index 10df60e36..50c301f21 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/PEG.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/PEG.json @@ -207,7 +207,7 @@ "properties": { "title": "PEG", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the PEG model. Information for the model is provided as follows: This model was a Simple Seasonal + Exponential Smoothing Model, with the GCC targets as inputs.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-12-22T00:00:00Z", "end_datetime": "2024-01-25T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/cb_prophet.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/cb_prophet.json index 43545ef59..831ca7fc2 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/cb_prophet.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/cb_prophet.json @@ -207,7 +207,7 @@ "properties": { "title": "cb_prophet", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-09T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json index fc0c62616..60474536f 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/climatology.json @@ -194,20 +194,20 @@ -149.2133, 63.8758 ], - [ - -149.3705, - 68.6611 - ], [ -156.6194, 71.2824 + ], + [ + -149.3705, + 68.6611 ] ] }, "properties": { "title": "climatology", - "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, BONA, DEJU, HEAL, TOOL, BARR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, BONA, DEJU, HEAL, BARR, TOOL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-07T00:00:00Z", "providers": [ @@ -283,8 +283,8 @@ "BONA", "DEJU", "HEAL", - "TOOL", - "BARR" + "BARR", + "TOOL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json index dee016597..17c9b3c89 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/persistenceRW.json @@ -207,7 +207,7 @@ "properties": { "title": "persistenceRW", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: DELA, DSNY, GRSM, GUAN, HARV, HEAL, BONA, CLBJ, CPER, DCFS, DEJU, SJER, SOAP, SRER, STEI, STER, TALL, NOGP, OAES, ONAQ, ORNL, OSBS, TEAK, TOOL, TREE, UKFS, UNDE, LAJA, LENO, MLBS, MOAB, NIWO, PUUM, RMNP, SCBI, SERC, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, JERC, JORN, KONA, KONZ.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json index bc3007f1d..7f94e6654 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_arima.json @@ -14,22 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -89.5373, - 46.2339 - ], - [ - -99.2413, - 47.1282 - ], - [ - -121.9519, - 45.8205 - ], - [ - -110.5391, - 44.9535 - ], [ -122.3303, 45.7624 @@ -201,19 +185,35 @@ [ -95.1921, 39.0404 + ], + [ + -89.5373, + 46.2339 + ], + [ + -99.2413, + 47.1282 + ], + [ + -121.9519, + 45.8205 + ], + [ + -110.5391, + 44.9535 ] ] }, "properties": { "title": "tg_arima", - "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Gregory Harrison", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", @@ -238,10 +238,6 @@ "gcc_90", "Daily", "P1D", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -284,7 +280,11 @@ "TEAK", "TOOL", "TREE", - "UKFS" + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json index 01e5361ac..9e6eb9979 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_ets.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_ets", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm.json index df5b4c6d6..01c618847 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm.json @@ -14,98 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 - ], - [ - -84.4686, - 31.1948 - ], - [ - -106.8425, - 32.5907 - ], - [ - -96.6129, - 39.1104 - ], - [ - -96.5631, - 39.1008 - ], - [ - -67.0769, - 18.0213 - ], - [ - -88.1612, - 31.8539 - ], - [ - -80.5248, - 37.3783 - ], - [ - -109.3883, - 38.2483 - ], - [ - -105.5824, - 40.0543 - ], - [ - -100.9154, - 46.7697 - ], - [ - -99.0588, - 35.4106 - ], - [ - -112.4524, - 40.1776 - ], - [ - -84.2826, - 35.9641 - ], - [ - -81.9934, - 29.6893 - ], - [ - -155.3173, - 19.5531 - ], - [ - -105.546, - 40.2759 - ], [ -78.1395, 38.8929 @@ -201,19 +109,111 @@ [ -99.1066, 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 + ], + [ + -84.4686, + 31.1948 + ], + [ + -106.8425, + 32.5907 + ], + [ + -96.6129, + 39.1104 + ], + [ + -96.5631, + 39.1008 + ], + [ + -67.0769, + 18.0213 + ], + [ + -88.1612, + 31.8539 + ], + [ + -80.5248, + 37.3783 + ], + [ + -109.3883, + 38.2483 + ], + [ + -105.5824, + 40.0543 + ], + [ + -100.9154, + 46.7697 + ], + [ + -99.0588, + 35.4106 + ], + [ + -112.4524, + 40.1776 + ], + [ + -84.2826, + 35.9641 + ], + [ + -81.9934, + 29.6893 + ], + [ + -155.3173, + 19.5531 + ], + [ + -105.546, + 40.2759 ] ] }, "properties": { "title": "tg_humidity_lm", - "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ { - "url": "https://projects.ecoforecast.org/neon4cast-ci/", - "name": "NEON Ecological Forecasting Project", + "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", + "name": "Abigail Lewis", "roles": [ "producer", "processor", @@ -238,29 +238,6 @@ "gcc_90", "Daily", "P1D", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA", - "KONZ", - "LAJA", - "LENO", - "MLBS", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", "SCBI", "SERC", "SJER", @@ -284,7 +261,30 @@ "BONA", "CLBJ", "CPER", - "DCFS" + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", + "KONA", + "KONZ", + "LAJA", + "LENO", + "MLBS", + "MOAB", + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm_all_sites.json index 6a09c4c2c..35639e295 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_humidity_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_lasso.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_lasso.json index 345c0da56..02d79c529 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_lasso.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_lasso.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_lasso", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm.json index 6b97bcf69..d2cca1fd5 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_precip_lm", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm_all_sites.json index 00cad00ab..8b1ee6386 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_precip_lm_all_sites.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "name": "Gregory Harrison", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_randfor.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_randfor.json index 860f40042..4de9b8630 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_randfor.json @@ -14,6 +14,42 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -109.3883, + 38.2483 + ], + [ + -105.5824, + 40.0543 + ], + [ + -100.9154, + 46.7697 + ], + [ + -99.0588, + 35.4106 + ], + [ + -112.4524, + 40.1776 + ], + [ + -84.2826, + 35.9641 + ], + [ + -81.9934, + 29.6893 + ], + [ + -155.3173, + 19.5531 + ], + [ + -105.546, + 40.2759 + ], [ -78.1395, 38.8929 @@ -165,49 +201,13 @@ [ -80.5248, 37.3783 - ], - [ - -109.3883, - 38.2483 - ], - [ - -105.5824, - 40.0543 - ], - [ - -100.9154, - 46.7697 - ], - [ - -99.0588, - 35.4106 - ], - [ - -112.4524, - 40.1776 - ], - [ - -84.2826, - 35.9641 - ], - [ - -81.9934, - 29.6893 - ], - [ - -155.3173, - 19.5531 - ], - [ - -105.546, - 40.2759 ] ] }, "properties": { "title": "tg_randfor", - "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -238,6 +238,15 @@ "gcc_90", "Daily", "P1D", + "MOAB", + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP", "SCBI", "SERC", "SJER", @@ -275,16 +284,7 @@ "KONZ", "LAJA", "LENO", - "MLBS", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP" + "MLBS" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json index d5da9e1b1..b06dd3f1f 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_tbats.json @@ -14,6 +14,14 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -121.9519, + 45.8205 + ], + [ + -110.5391, + 44.9535 + ], [ -122.3303, 45.7624 @@ -193,21 +201,13 @@ [ -99.2413, 47.1282 - ], - [ - -121.9519, - 45.8205 - ], - [ - -110.5391, - 44.9535 ] ] }, "properties": { "title": "tg_tbats", - "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -238,6 +238,8 @@ "gcc_90", "Daily", "P1D", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -282,9 +284,7 @@ "TREE", "UKFS", "UNDE", - "WOOD", - "WREF", - "YELL" + "WOOD" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm.json index fa55f245c..3f89907a2 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm.json @@ -14,6 +14,70 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -78.1395, + 38.8929 + ], + [ + -76.56, + 38.8901 + ], + [ + -119.7323, + 37.1088 + ], + [ + -119.2622, + 37.0334 + ], + [ + -110.8355, + 31.9107 + ], + [ + -89.5864, + 45.5089 + ], + [ + -103.0293, + 40.4619 + ], + [ + -87.3933, + 32.9505 + ], + [ + -119.006, + 37.0058 + ], + [ + -149.3705, + 68.6611 + ], + [ + -89.5857, + 45.4937 + ], + [ + -95.1921, + 39.0404 + ], + [ + -89.5373, + 46.2339 + ], + [ + -99.2413, + 47.1282 + ], + [ + -121.9519, + 45.8205 + ], + [ + -110.5391, + 44.9535 + ], [ -122.3303, 45.7624 @@ -137,77 +201,13 @@ [ -105.546, 40.2759 - ], - [ - -78.1395, - 38.8929 - ], - [ - -76.56, - 38.8901 - ], - [ - -119.7323, - 37.1088 - ], - [ - -119.2622, - 37.0334 - ], - [ - -110.8355, - 31.9107 - ], - [ - -89.5864, - 45.5089 - ], - [ - -103.0293, - 40.4619 - ], - [ - -87.3933, - 32.9505 - ], - [ - -119.006, - 37.0058 - ], - [ - -149.3705, - 68.6611 - ], - [ - -89.5857, - 45.4937 - ], - [ - -95.1921, - 39.0404 - ], - [ - -89.5373, - 46.2339 - ], - [ - -99.2413, - 47.1282 - ], - [ - -121.9519, - 45.8205 - ], - [ - -110.5391, - 44.9535 ] ] }, "properties": { "title": "tg_temp_lm", - "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -238,6 +238,22 @@ "gcc_90", "Daily", "P1D", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -268,23 +284,7 @@ "ORNL", "OSBS", "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "RMNP" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm_all_sites.json index 80addf799..ffb9ba2d3 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Green_chromatic_coordinate/models/tg_temp_lm_all_sites.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/collection.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/collection.json index 15e9e7fad..d4651ed84 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/collection.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/collection.json @@ -11,17 +11,12 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_tbats.json" - }, - { - "rel": "item", - "type": "application/json", - "href": "./models/tg_temp_lm.json" + "href": "./models/baseline_ensemble.json" }, { "rel": "item", "type": "application/json", - "href": "./models/baseline_ensemble.json" + "href": "./models/tg_arima.json" }, { "rel": "item", @@ -46,37 +41,42 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm.json" + "href": "./models/cb_prophet.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/tg_precip_lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/cb_prophet.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/tg_tbats.json" + }, + { + "rel": "item", + "type": "application/json", + "href": "./models/tg_temp_lm.json" }, { "rel": "item", diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/PEG.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/PEG.json index 41e9e7195..95e2290c6 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/PEG.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/PEG.json @@ -207,7 +207,7 @@ "properties": { "title": "PEG", "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the PEG model. Information for the model is provided as follows: This model was a Simple Seasonal + Exponential Smoothing Model, with the GCC targets as inputs.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-12-22T00:00:00Z", "end_datetime": "2024-01-25T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json index 0471bc7bd..b9e1d693c 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/baseline_ensemble.json @@ -182,6 +182,10 @@ -100.9154, 46.7697 ], + [ + -156.6194, + 71.2824 + ], [ -147.5026, 65.154 @@ -197,17 +201,13 @@ [ -149.3705, 68.6611 - ], - [ - -156.6194, - 71.2824 ] ] }, "properties": { "title": "baseline_ensemble", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, SERC, SJER, SOAP, SRER, STEI, UNDE, WOOD, WREF, YELL, STER, TALL, TEAK, TREE, UKFS, DELA, DSNY, GRSM, GUAN, MLBS, MOAB, NIWO, NOGP, BONA, DEJU, HEAL, TOOL, BARR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, SERC, SJER, SOAP, SRER, STEI, UNDE, WOOD, WREF, YELL, STER, TALL, TEAK, TREE, UKFS, DELA, DSNY, GRSM, GUAN, MLBS, MOAB, NIWO, NOGP, BARR, BONA, DEJU, HEAL, TOOL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -280,11 +280,11 @@ "MOAB", "NIWO", "NOGP", + "BARR", "BONA", "DEJU", "HEAL", - "TOOL", - "BARR" + "TOOL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/cb_prophet.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/cb_prophet.json index 0eae9e35d..8a5e7d3c1 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/cb_prophet.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/cb_prophet.json @@ -207,7 +207,7 @@ "properties": { "title": "cb_prophet", "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-09T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json index 4c6a17d09..56bb4fa59 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/climatology.json @@ -194,20 +194,20 @@ -147.5026, 65.154 ], - [ - -149.3705, - 68.6611 - ], [ -156.6194, 71.2824 + ], + [ + -149.3705, + 68.6611 ] ] }, "properties": { "title": "climatology", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, DEJU, HEAL, BONA, TOOL, BARR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, DEJU, HEAL, BONA, BARR, TOOL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-07T00:00:00Z", "providers": [ @@ -283,8 +283,8 @@ "DEJU", "HEAL", "BONA", - "TOOL", - "BARR" + "BARR", + "TOOL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json index f00d28187..96784f48e 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/persistenceRW.json @@ -14,6 +14,42 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -105.546, + 40.2759 + ], + [ + -78.1395, + 38.8929 + ], + [ + -76.56, + 38.8901 + ], + [ + -119.7323, + 37.1088 + ], + [ + -119.2622, + 37.0334 + ], + [ + -84.4686, + 31.1948 + ], + [ + -106.8425, + 32.5907 + ], [ -96.6129, 39.1104 @@ -43,28 +79,24 @@ 44.9535 ], [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 + -110.8355, + 31.9107 ], [ - -71.2874, - 44.0639 + -89.5864, + 45.5089 ], [ - -78.0418, - 39.0337 + -103.0293, + 40.4619 ], [ - -147.5026, - 65.154 + -87.3933, + 32.9505 ], [ - -97.57, - 33.4012 + -119.006, + 37.0058 ], [ -104.7456, @@ -86,6 +118,10 @@ -81.4362, 28.1251 ], + [ + -99.0588, + 35.4106 + ], [ -112.4524, 40.1776 @@ -102,58 +138,6 @@ -155.3173, 19.5531 ], - [ - -110.8355, - 31.9107 - ], - [ - -89.5864, - 45.5089 - ], - [ - -103.0293, - 40.4619 - ], - [ - -87.3933, - 32.9505 - ], - [ - -119.006, - 37.0058 - ], - [ - -105.546, - 40.2759 - ], - [ - -78.1395, - 38.8929 - ], - [ - -76.56, - 38.8901 - ], - [ - -119.7323, - 37.1088 - ], - [ - -119.2622, - 37.0334 - ], - [ - -99.0588, - 35.4106 - ], - [ - -84.4686, - 31.1948 - ], - [ - -106.8425, - 32.5907 - ], [ -80.5248, 37.3783 @@ -186,6 +170,22 @@ -89.5373, 46.2339 ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], [ -83.5019, 35.689 @@ -206,8 +206,8 @@ }, "properties": { "title": "persistenceRW", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: KONA, KONZ, LAJA, LENO, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, ONAQ, ORNL, OSBS, PUUM, SRER, STEI, STER, TALL, TEAK, RMNP, SCBI, SERC, SJER, SOAP, OAES, JERC, JORN, MLBS, MOAB, NIWO, NOGP, TOOL, TREE, UKFS, UNDE, GRSM, GUAN, HARV, HEAL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: ABBY, BARR, RMNP, SCBI, SERC, SJER, SOAP, JERC, JORN, KONA, KONZ, LAJA, LENO, WOOD, WREF, YELL, SRER, STEI, STER, TALL, TEAK, CPER, DCFS, DEJU, DELA, DSNY, OAES, ONAQ, ORNL, OSBS, PUUM, MLBS, MOAB, NIWO, NOGP, TOOL, TREE, UKFS, UNDE, BART, BLAN, BONA, CLBJ, GRSM, GUAN, HARV, HEAL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ @@ -238,6 +238,15 @@ "rcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "JERC", + "JORN", "KONA", "KONZ", "LAJA", @@ -245,34 +254,21 @@ "WOOD", "WREF", "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", "CPER", "DCFS", "DEJU", "DELA", "DSNY", + "OAES", "ONAQ", "ORNL", "OSBS", "PUUM", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "OAES", - "JERC", - "JORN", "MLBS", "MOAB", "NIWO", @@ -281,6 +277,10 @@ "TREE", "UKFS", "UNDE", + "BART", + "BLAN", + "BONA", + "CLBJ", "GRSM", "GUAN", "HARV", diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json index d7c266c5e..a272fd141 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_arima.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_arima", "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Gregory Harrison", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json index 18e1d940e..0d7e55161 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_ets.json @@ -14,6 +14,74 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 + ], + [ + -84.4686, + 31.1948 + ], + [ + -106.8425, + 32.5907 + ], [ -96.6129, 39.1104 @@ -133,81 +201,13 @@ [ -110.5391, 44.9535 - ], - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 - ], - [ - -84.4686, - 31.1948 - ], - [ - -106.8425, - 32.5907 ] ] }, "properties": { "title": "tg_ets", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -238,6 +238,23 @@ "rcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", "KONA", "KONZ", "LAJA", @@ -267,24 +284,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN" + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm.json index 0b5b72e75..695a2cbfb 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_humidity_lm", "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ { - "url": "https://projects.ecoforecast.org/neon4cast-ci/", - "name": "NEON Ecological Forecasting Project", + "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", + "name": "Abigail Lewis", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm_all_sites.json index 26a552439..6312d6670 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_humidity_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_lasso.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_lasso.json index c5879433a..412135dcf 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_lasso.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_lasso.json @@ -14,70 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 - ], - [ - -84.4686, - 31.1948 - ], [ -106.8425, 32.5907 @@ -201,13 +137,77 @@ [ -110.5391, 44.9535 + ], + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 + ], + [ + -84.4686, + 31.1948 ] ] }, "properties": { "title": "tg_lasso", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -238,22 +238,6 @@ "rcc_90", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", "JORN", "KONA", "KONZ", @@ -284,7 +268,23 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm.json index 98ebcaf3d..c08b105b0 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_precip_lm", "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm_all_sites.json index a441e27e9..4451f0769 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_precip_lm_all_sites.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "name": "Gregory Harrison", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_randfor.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_randfor.json index cbd587055..91f80b25f 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_randfor.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_randfor", "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json index e5bd2304f..d05e0e997 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_tbats.json @@ -14,22 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -89.5373, - 46.2339 - ], - [ - -99.2413, - 47.1282 - ], - [ - -121.9519, - 45.8205 - ], - [ - -110.5391, - 44.9535 - ], [ -122.3303, 45.7624 @@ -201,13 +185,29 @@ [ -95.1921, 39.0404 + ], + [ + -89.5373, + 46.2339 + ], + [ + -99.2413, + 47.1282 + ], + [ + -121.9519, + 45.8205 + ], + [ + -110.5391, + 44.9535 ] ] }, "properties": { "title": "tg_tbats", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -238,10 +238,6 @@ "rcc_90", "Daily", "P1D", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -284,7 +280,11 @@ "TEAK", "TOOL", "TREE", - "UKFS" + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm.json index a0495976c..4f594a58e 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm_all_sites.json index 26dc90e92..835c4ef61 100644 --- a/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Phenology/Daily_Red_chromatic_coordinate/models/tg_temp_lm_all_sites.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/30min_Net_ecosystem_exchange/models/climatology.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/30min_Net_ecosystem_exchange/models/climatology.json index cd558c647..733abf4e8 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/30min_Net_ecosystem_exchange/models/climatology.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/30min_Net_ecosystem_exchange/models/climatology.json @@ -207,7 +207,7 @@ "properties": { "title": "climatology", "description": "All summaries for the 30min_Net_ecosystem_exchange variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-01-09T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/30min_latent_heat_flux/models/climatology.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/30min_latent_heat_flux/models/climatology.json index 9cd71f70b..666437e4f 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/30min_latent_heat_flux/models/climatology.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/30min_latent_heat_flux/models/climatology.json @@ -14,6 +14,10 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -76.56, + 38.8901 + ], [ -119.7323, 37.1088 @@ -30,6 +34,14 @@ -89.5864, 45.5089 ], + [ + -103.0293, + 40.4619 + ], + [ + -87.3933, + 32.9505 + ], [ -119.006, 37.0058 @@ -74,14 +86,6 @@ -71.2874, 44.0639 ], - [ - -103.0293, - 40.4619 - ], - [ - -87.3933, - 32.9505 - ], [ -78.0418, 39.0337 @@ -150,6 +154,14 @@ -67.0769, 18.0213 ], + [ + -78.1395, + 38.8929 + ], + [ + -105.546, + 40.2759 + ], [ -88.1612, 31.8539 @@ -189,25 +201,13 @@ [ -155.3173, 19.5531 - ], - [ - -105.546, - 40.2759 - ], - [ - -78.1395, - 38.8929 - ], - [ - -76.56, - 38.8901 ] ] }, "properties": { "title": "climatology", - "description": "All summaries for the 30min_latent_heat_flux variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: SJER, SOAP, SRER, STEI, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, STER, TALL, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All summaries for the 30min_latent_heat_flux variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, SCBI, RMNP, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-01-09T00:00:00Z", "providers": [ @@ -238,10 +238,13 @@ "le", "30min", "PT30M", + "SERC", "SJER", "SOAP", "SRER", "STEI", + "STER", + "TALL", "TEAK", "TOOL", "TREE", @@ -253,8 +256,6 @@ "ABBY", "BARR", "BART", - "STER", - "TALL", "BLAN", "BONA", "CLBJ", @@ -272,6 +273,8 @@ "KONA", "KONZ", "LAJA", + "SCBI", + "RMNP", "LENO", "MLBS", "MOAB", @@ -281,10 +284,7 @@ "ONAQ", "ORNL", "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC" + "PUUM" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/USUNEEDAILY.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/USUNEEDAILY.json index 2d8b31a93..5253afec6 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/USUNEEDAILY.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/USUNEEDAILY.json @@ -23,7 +23,7 @@ "properties": { "title": "USUNEEDAILY", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the USUNEEDAILY model. Information for the model is provided as follows: \"Home brew ARIMA.\" We didn't use a formal time series framework because of all the missing values in both our response variable and the weather covariates. So we used a GAM to fit a seasonal component based on day of year, and we included NEE the previous day as as an AR 1 term. We did some model selection, using cross validation, to identify temperature and relative humidity as weather covariates..\n The model predicts this variable at the following sites: PUUM.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-12-12T00:00:00Z", "end_datetime": "2024-01-16T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json index b82d06a8a..a19cd8020 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/bookcast_forest.json @@ -27,7 +27,7 @@ "properties": { "title": "bookcast_forest", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the bookcast_forest model. Information for the model is provided as follows: A simple daily timestep process-based model of a terrestrial carbon cycle. It includes leaves, wood, and soil pools. It uses a light-use efficiency GPP model to convert PAR to carbon. The model is derived from https://github.com/mdietze/FluxCourseForecast..\n The model predicts this variable at the following sites: TALL, OSBS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2024-01-10T00:00:00Z", "end_datetime": "2024-07-12T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/cb_prophet.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/cb_prophet.json index 4246740fc..ebdd33fa4 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/cb_prophet.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/cb_prophet.json @@ -203,7 +203,7 @@ "properties": { "title": "cb_prophet", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: PUUM, GUAN, OSBS, SCBI, MOAB, BART, CPER, HARV, UNDE, STER, KONA, TREE, ABBY, LENO, UKFS, DEJU, KONZ, RMNP, BARR, JORN, SOAP, STEI, TALL, DCFS, TOOL, WOOD, OAES, HEAL, SERC, BLAN, GRSM, ORNL, SRER, NOGP, JERC, DELA, MLBS, NIWO, WREF, LAJA, TEAK, CLBJ, SJER, ONAQ, DSNY, BONA.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/climatology.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/climatology.json index 6f7d8bafb..af32183f3 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/climatology.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/climatology.json @@ -207,7 +207,7 @@ "properties": { "title": "climatology", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json index f2ffa36ed..fc71ab1a8 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/persistenceRW.json @@ -207,7 +207,7 @@ "properties": { "title": "persistenceRW", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: NOGP, OAES, ONAQ, ORNL, OSBS, UNDE, WOOD, WREF, YELL, BONA, CLBJ, CPER, DCFS, DEJU, DELA, HEAL, JERC, JORN, KONA, KONZ, LAJA, SJER, SOAP, SRER, STEI, STER, TALL, DSNY, GRSM, GUAN, HARV, TEAK, TOOL, TREE, UKFS, ABBY, BARR, BART, BLAN, LENO, MLBS, MOAB, NIWO, PUUM, RMNP, SCBI, SERC.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-06T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json index a136be806..0acae062c 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_arima.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_arima", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Gregory Harrison", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json index 1790b4912..dda1400e8 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_ets.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_ets", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm.json index 6444877b9..e543eeae1 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_humidity_lm", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ { - "url": "https://projects.ecoforecast.org/neon4cast-ci/", - "name": "NEON Ecological Forecasting Project", + "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", + "name": "Abigail Lewis", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm_all_sites.json index 80ad42a9c..d228a3974 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_humidity_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm.json index 9f01c3576..baaea6221 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_precip_lm", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm_all_sites.json index 024fa23d2..51164f529 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_precip_lm_all_sites.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "name": "Gregory Harrison", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_randfor.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_randfor.json index 5729af50e..27db693ce 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_randfor.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_randfor", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json index d74b2ab2e..3a7d2fc7b 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_tbats.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_tbats", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm.json index 62dffc402..41017d0ca 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm_all_sites.json index a92dd5d27..3939fc55f 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_Net_ecosystem_exchange/models/tg_temp_lm_all_sites.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries for the Daily_Net_ecosystem_exchange variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/collection.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/collection.json index 7f99bc4bb..2f231e197 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/collection.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/collection.json @@ -26,22 +26,22 @@ { "rel": "item", "type": "application/json", - "href": "./models/tg_arima.json" + "href": "./models/tg_precip_lm_all_sites.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_ets.json" + "href": "./models/tg_randfor.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_precip_lm_all_sites.json" + "href": "./models/tg_arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/tg_randfor.json" + "href": "./models/tg_ets.json" }, { "rel": "item", diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/cb_prophet.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/cb_prophet.json index 2cbbb927d..2710accbd 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/cb_prophet.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/cb_prophet.json @@ -14,18 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -97.57, - 33.4012 - ], - [ - -119.7323, - 37.1088 - ], - [ - -112.4524, - 40.1776 - ], [ -81.4362, 28.1251 @@ -46,10 +34,6 @@ -66.8687, 17.9696 ], - [ - -81.9934, - 29.6893 - ], [ -71.2874, 44.0639 @@ -194,16 +178,32 @@ -119.006, 37.0058 ], + [ + -97.57, + 33.4012 + ], + [ + -119.7323, + 37.1088 + ], + [ + -81.9934, + 29.6893 + ], [ -147.5026, 65.154 + ], + [ + -112.4524, + 40.1776 ] ] }, "properties": { "title": "cb_prophet", - "description": "All summaries for the Daily_latent_heat_flux variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: CLBJ, SJER, ONAQ, DSNY, SCBI, MOAB, PUUM, GUAN, OSBS, BART, CPER, HARV, UNDE, STER, KONA, TREE, ABBY, LENO, UKFS, DEJU, KONZ, RMNP, BARR, JORN, SOAP, STEI, TALL, DCFS, TOOL, WOOD, OAES, HEAL, SERC, BLAN, GRSM, ORNL, SRER, NOGP, JERC, DELA, MLBS, NIWO, WREF, LAJA, TEAK, BONA.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All summaries for the Daily_latent_heat_flux variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: DSNY, SCBI, MOAB, PUUM, GUAN, BART, CPER, HARV, UNDE, STER, KONA, TREE, ABBY, LENO, UKFS, DEJU, KONZ, RMNP, BARR, JORN, SOAP, STEI, TALL, DCFS, TOOL, WOOD, OAES, HEAL, SERC, BLAN, GRSM, ORNL, SRER, NOGP, JERC, DELA, MLBS, NIWO, WREF, LAJA, TEAK, CLBJ, SJER, OSBS, BONA, ONAQ.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ @@ -234,15 +234,11 @@ "le", "Daily", "P1D", - "CLBJ", - "SJER", - "ONAQ", "DSNY", "SCBI", "MOAB", "PUUM", "GUAN", - "OSBS", "BART", "CPER", "HARV", @@ -279,7 +275,11 @@ "WREF", "LAJA", "TEAK", - "BONA" + "CLBJ", + "SJER", + "OSBS", + "BONA", + "ONAQ" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/climatology.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/climatology.json index aa202cdba..af44f2c78 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/climatology.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/climatology.json @@ -207,7 +207,7 @@ "properties": { "title": "climatology", "description": "All summaries for the Daily_latent_heat_flux variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-15T00:00:00Z", "end_datetime": "2024-08-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json index 6113369f0..392b174ca 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_arima.json @@ -14,6 +14,66 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -67.0769, + 18.0213 + ], + [ + -88.1612, + 31.8539 + ], + [ + -80.5248, + 37.3783 + ], + [ + -109.3883, + 38.2483 + ], + [ + -105.5824, + 40.0543 + ], + [ + -100.9154, + 46.7697 + ], + [ + -99.0588, + 35.4106 + ], + [ + -112.4524, + 40.1776 + ], + [ + -84.2826, + 35.9641 + ], + [ + -81.9934, + 29.6893 + ], + [ + -155.3173, + 19.5531 + ], + [ + -105.546, + 40.2759 + ], + [ + -78.1395, + 38.8929 + ], + [ + -76.56, + 38.8901 + ], + [ + -119.7323, + 37.1088 + ], [ -119.2622, 37.0334 @@ -141,79 +201,19 @@ [ -96.5631, 39.1008 - ], - [ - -67.0769, - 18.0213 - ], - [ - -88.1612, - 31.8539 - ], - [ - -80.5248, - 37.3783 - ], - [ - -109.3883, - 38.2483 - ], - [ - -105.5824, - 40.0543 - ], - [ - -100.9154, - 46.7697 - ], - [ - -99.0588, - 35.4106 - ], - [ - -112.4524, - 40.1776 - ], - [ - -84.2826, - 35.9641 - ], - [ - -81.9934, - 29.6893 - ], - [ - -155.3173, - 19.5531 - ], - [ - -105.546, - 40.2759 - ], - [ - -78.1395, - 38.8929 - ], - [ - -76.56, - 38.8901 - ], - [ - -119.7323, - 37.1088 ] ] }, "properties": { "title": "tg_arima", - "description": "All summaries for the Daily_latent_heat_flux variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All summaries for the Daily_latent_heat_flux variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Gregory Harrison", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", @@ -238,6 +238,21 @@ "le", "Daily", "P1D", + "LAJA", + "LENO", + "MLBS", + "MOAB", + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER", "SOAP", "SRER", "STEI", @@ -269,22 +284,7 @@ "JERC", "JORN", "KONA", - "KONZ", - "LAJA", - "LENO", - "MLBS", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER" + "KONZ" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json index 440c09904..61fc63b9a 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_ets.json @@ -14,98 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 - ], - [ - -84.4686, - 31.1948 - ], - [ - -106.8425, - 32.5907 - ], - [ - -96.6129, - 39.1104 - ], - [ - -96.5631, - 39.1008 - ], - [ - -67.0769, - 18.0213 - ], - [ - -88.1612, - 31.8539 - ], - [ - -80.5248, - 37.3783 - ], - [ - -109.3883, - 38.2483 - ], [ -105.5824, 40.0543 @@ -201,13 +109,105 @@ [ -110.5391, 44.9535 + ], + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 + ], + [ + -84.4686, + 31.1948 + ], + [ + -106.8425, + 32.5907 + ], + [ + -96.6129, + 39.1104 + ], + [ + -96.5631, + 39.1008 + ], + [ + -67.0769, + 18.0213 + ], + [ + -88.1612, + 31.8539 + ], + [ + -80.5248, + 37.3783 + ], + [ + -109.3883, + 38.2483 ] ] }, "properties": { "title": "tg_ets", - "description": "All summaries for the Daily_latent_heat_flux variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All summaries for the Daily_latent_heat_flux variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-07T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ @@ -238,29 +238,6 @@ "le", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA", - "KONZ", - "LAJA", - "LENO", - "MLBS", - "MOAB", "NIWO", "NOGP", "OAES", @@ -284,7 +261,30 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", + "KONA", + "KONZ", + "LAJA", + "LENO", + "MLBS", + "MOAB" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm.json index e1c75ec92..240c0f1a0 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm.json @@ -14,18 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 - ], - [ - -84.4686, - 31.1948 - ], [ -122.3303, 45.7624 @@ -78,6 +66,18 @@ -66.8687, 17.9696 ], + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 + ], + [ + -84.4686, + 31.1948 + ], [ -106.8425, 32.5907 @@ -206,14 +206,14 @@ }, "properties": { "title": "tg_humidity_lm", - "description": "All summaries for the Daily_latent_heat_flux variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: HARV, HEAL, JERC, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All summaries for the Daily_latent_heat_flux variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ { - "url": "https://projects.ecoforecast.org/neon4cast-ci/", - "name": "NEON Ecological Forecasting Project", + "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", + "name": "Abigail Lewis", "roles": [ "producer", "processor", @@ -238,9 +238,6 @@ "le", "Daily", "P1D", - "HARV", - "HEAL", - "JERC", "ABBY", "BARR", "BART", @@ -254,6 +251,9 @@ "DSNY", "GRSM", "GUAN", + "HARV", + "HEAL", + "JERC", "JORN", "KONA", "KONZ", diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm_all_sites.json index ff7bd6bab..6a8f907fe 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_humidity_lm_all_sites.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All summaries for the Daily_latent_heat_flux variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm.json index eb63391b1..a0d038e23 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm.json @@ -14,66 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -122.3303, - 45.7624 - ], - [ - -156.6194, - 71.2824 - ], - [ - -71.2874, - 44.0639 - ], - [ - -78.0418, - 39.0337 - ], - [ - -147.5026, - 65.154 - ], - [ - -97.57, - 33.4012 - ], - [ - -104.7456, - 40.8155 - ], - [ - -99.1066, - 47.1617 - ], - [ - -145.7514, - 63.8811 - ], - [ - -87.8039, - 32.5417 - ], - [ - -81.4362, - 28.1251 - ], - [ - -83.5019, - 35.689 - ], - [ - -66.8687, - 17.9696 - ], - [ - -72.1727, - 42.5369 - ], - [ - -149.2133, - 63.8758 - ], [ -84.4686, 31.1948 @@ -201,13 +141,73 @@ [ -110.5391, 44.9535 + ], + [ + -122.3303, + 45.7624 + ], + [ + -156.6194, + 71.2824 + ], + [ + -71.2874, + 44.0639 + ], + [ + -78.0418, + 39.0337 + ], + [ + -147.5026, + 65.154 + ], + [ + -97.57, + 33.4012 + ], + [ + -104.7456, + 40.8155 + ], + [ + -99.1066, + 47.1617 + ], + [ + -145.7514, + 63.8811 + ], + [ + -87.8039, + 32.5417 + ], + [ + -81.4362, + 28.1251 + ], + [ + -83.5019, + 35.689 + ], + [ + -66.8687, + 17.9696 + ], + [ + -72.1727, + 42.5369 + ], + [ + -149.2133, + 63.8758 ] ] }, "properties": { "title": "tg_precip_lm", - "description": "All summaries for the Daily_latent_heat_flux variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "description": "All summaries for the Daily_latent_heat_flux variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ @@ -238,21 +238,6 @@ "le", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", "JERC", "JORN", "KONA", @@ -284,7 +269,22 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL" ], "table:columns": [ { diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm_all_sites.json index d5fdcae65..c34cf4289 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_precip_lm_all_sites.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All summaries for the Daily_latent_heat_flux variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "name": "Gregory Harrison", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_randfor.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_randfor.json index 91f83a767..d44058f60 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_randfor.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_randfor", "description": "All summaries for the Daily_latent_heat_flux variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-04T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json index 238f4ef27..847b9592f 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_tbats.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_tbats", "description": "All summaries for the Daily_latent_heat_flux variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-01T00:00:00Z", "end_datetime": "2024-08-02T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm.json index 0e96069cf..db8d10fd7 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm.json @@ -207,7 +207,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries for the Daily_latent_heat_flux variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-08T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm_all_sites.json index 914620c60..7804b0770 100644 --- a/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Terrestrial/Daily_latent_heat_flux/models/tg_temp_lm_all_sites.json @@ -207,13 +207,13 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries for the Daily_latent_heat_flux variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-14T00:00:00Z", "end_datetime": "2024-03-05T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_arima.json b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_arima.json index 561040fdc..bf7b934e7 100644 --- a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_arima.json +++ b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_arima.json @@ -55,13 +55,13 @@ "properties": { "title": "tg_arima", "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-02-13T00:00:00Z", "end_datetime": "2025-06-23T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Gregory Harrison", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_ets.json b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_ets.json index 1222dc474..d7b6cedb0 100644 --- a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_ets.json +++ b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_ets.json @@ -55,7 +55,7 @@ "properties": { "title": "tg_ets", "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-02-06T00:00:00Z", "end_datetime": "2025-06-23T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm.json b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm.json index 5917b0841..4985e42b8 100644 --- a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm.json @@ -55,13 +55,13 @@ "properties": { "title": "tg_humidity_lm", "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ { - "url": "https://projects.ecoforecast.org/neon4cast-ci/", - "name": "NEON Ecological Forecasting Project", + "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", + "name": "Abigail Lewis", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm_all_sites.json index d384f8531..6b7c098e3 100644 --- a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_humidity_lm_all_sites.json @@ -55,7 +55,7 @@ "properties": { "title": "tg_humidity_lm_all_sites", "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_humidity_lm_all_sites model. Information for the model is provided as follows: The tg_humidity_lm_all_sites model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity. This model was used to forecast water temperature and dissolved oxygen concentration at the\nseven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_lasso.json b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_lasso.json index 02de4868f..ebfbd67f3 100644 --- a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_lasso.json +++ b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_lasso.json @@ -51,7 +51,7 @@ "properties": { "title": "tg_lasso", "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: BLAN, KONZ, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm.json b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm.json index 3a696f4b2..3c5d47184 100644 --- a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm.json @@ -55,7 +55,7 @@ "properties": { "title": "tg_precip_lm", "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm_all_sites.json index 7e93eb25c..d0751b227 100644 --- a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_precip_lm_all_sites.json @@ -55,13 +55,13 @@ "properties": { "title": "tg_precip_lm_all_sites", "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ { "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "name": "Gregory Harrison", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_randfor.json b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_randfor.json index f11eec646..b4f0b476f 100644 --- a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_randfor.json +++ b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_randfor.json @@ -51,7 +51,7 @@ "properties": { "title": "tg_randfor", "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: BLAN, KONZ, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-19T00:00:00Z", "end_datetime": "2024-03-01T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_tbats.json b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_tbats.json index 40f6df79e..50039e242 100644 --- a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_tbats.json +++ b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_tbats.json @@ -55,7 +55,7 @@ "properties": { "title": "tg_tbats", "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-01-02T00:00:00Z", "end_datetime": "2025-06-23T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm.json b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm.json index fa5785079..2ccf39f1e 100644 --- a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm.json +++ b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm.json @@ -55,7 +55,7 @@ "properties": { "title": "tg_temp_lm", "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ diff --git a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm_all_sites.json b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm_all_sites.json index 0ebf59fa8..83b24b167 100644 --- a/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm_all_sites.json +++ b/data/challenge/neon4cast-stac/summaries/Ticks/Weekly_Amblyomma_americanum_population/models/tg_temp_lm_all_sites.json @@ -55,13 +55,13 @@ "properties": { "title": "tg_temp_lm_all_sites", "description": "All summaries for the Weekly_Amblyomma_americanum_population variable for the tg_temp_lm_all_sites model. Information for the model is provided as follows: The tg_temp_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation.This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together.\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", - "datetime": "2024-09-06T00:00:00Z", + "datetime": "2024-09-07T00:00:00Z", "start_datetime": "2023-11-20T00:00:00Z", "end_datetime": "2024-02-26T00:00:00Z", "providers": [ { - "url": "https://github.com/eco4cast/Forecast_submissions/blob/main/Generate_forecasts", - "name": "Abigail Lewis", + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", "roles": [ "producer", "processor", diff --git a/data/challenge/neon4cast-stac/targets/collection.json b/data/challenge/neon4cast-stac/targets/collection.json index 14f6efe24..6e8c2dc2f 100644 --- a/data/challenge/neon4cast-stac/targets/collection.json +++ b/data/challenge/neon4cast-stac/targets/collection.json @@ -58,7 +58,7 @@ "interval": [ [ "2013-03-07T00:00:00Z", - "2024-09-04T00:00:00Z" + "2024-09-05T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/collection.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/collection.json index 5d4b7cf60..c9c7b0957 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/collection.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/collection.json @@ -16,77 +16,77 @@ { "rel": "item", "type": "application/json", - "href": "./models/asl.ets.json" + "href": "./models/asl.climate.window.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.json" + "href": "./models/asl.ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.tbats.json" + "href": "./models/asl.met.lm.step.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.temp.lm.json" + "href": "./models/asl.met.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/asl.persistence.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableARIMA.json" + "href": "./models/asl.tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableETS.json" + "href": "./models/asl.temp.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableNNETAR.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/glm_aed_v1.json" + "href": "./models/fableARIMA.json" }, { "rel": "item", "type": "application/json", - "href": "./models/historic_mean.json" + "href": "./models/fableETS.json" }, { "rel": "item", "type": "application/json", - "href": "./models/monthly_mean.json" + "href": "./models/fableNNETAR.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/glm_aed_v1.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.climate.window.json" + "href": "./models/historic_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.step.json" + "href": "./models/monthly_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.persistence.json" + "href": "./models/persistenceRW.json" }, { "rel": "parent", @@ -136,7 +136,7 @@ "interval": [ [ "2023-10-01T00:00:00Z", - "2024-10-10T00:00:00Z" + "2024-10-11T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.auto.arima.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.auto.arima.json index 04fcc2ae5..8fe474f58 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.auto.arima.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.auto.arima.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.auto.arima", "description": "All forecasts for the Daily_Bloom_binary variable for the asl.auto.arima model. Information for the model is provided as follows: forecast::auto.arima() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-04-29T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.climate.window.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.climate.window.json index 3551ae9b0..9f5224b02 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.climate.window.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.climate.window.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.climate.window", "description": "All forecasts for the Daily_Bloom_binary variable for the asl.climate.window model. Information for the model is provided as follows: Climatology, but with a rolling 10-day window for historical data. This works really well for my methane forecasts at SERC, so I thought it might be useful here.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-09-04T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.ets.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.ets.json index 10a34e738..9d1696506 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.ets.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.ets.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.ets", "description": "All forecasts for the Daily_Bloom_binary variable for the asl.ets model. Information for the model is provided as follows: forecast::ets() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-04-29T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.met.lm.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.met.lm.json index 0e924a57f..ed4daf5c3 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.met.lm.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.met.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm", "description": "All forecasts for the Daily_Bloom_binary variable for the asl.met.lm model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-14T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.met.lm.step.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.met.lm.step.json index 83097894f..672259274 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.met.lm.step.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.met.lm.step.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm.step", "description": "All forecasts for the Daily_Bloom_binary variable for the asl.met.lm.step model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-14T00:00:00Z", "end_datetime": "2024-06-19T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.persistence.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.persistence.json index 364318b37..cc31ead6b 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.persistence.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.persistence.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.persistence", "description": "All forecasts for the Daily_Bloom_binary variable for the asl.persistence model. Information for the model is provided as follows: The random walk persistence model has massive uncertainty at long horizons, but you could instead re-fit the persistence model for every forecast horizon to get a more precise forecast (i.e., not dynamic). I did this for my work at SERC, and am submitting here in case it is helpful.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-09-05T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.tbats.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.tbats.json index 3beeddefe..ff038a71f 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.tbats.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.tbats.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.tbats", "description": "All forecasts for the Daily_Bloom_binary variable for the asl.tbats model. Information for the model is provided as follows: forecast::tbats() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-04-29T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.temp.lm.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.temp.lm.json index f85ae6500..8f4d8b229 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.temp.lm.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/asl.temp.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.temp.lm", "description": "All forecasts for the Daily_Bloom_binary variable for the asl.temp.lm model. Information for the model is provided as follows: Linear regression with air temperature.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-14T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/climatology.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/climatology.json index 80ee722dd..dff44524f 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/climatology.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/climatology.json @@ -27,9 +27,9 @@ "properties": { "title": "climatology", "description": "All forecasts for the Daily_Bloom_binary variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-09T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/fableARIMA.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/fableARIMA.json index 6b4d0cd72..a2ae7ba66 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/fableARIMA.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/fableARIMA.json @@ -27,9 +27,9 @@ "properties": { "title": "fableARIMA", "description": "All forecasts for the Daily_Bloom_binary variable for the fableARIMA model. Information for the model is provided as follows: ARIMA fit using the ARIMA() function in the fable R package.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableARIMA.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/fableETS.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/fableETS.json index c72f52e9e..df1918f3a 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/fableETS.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/fableETS.json @@ -27,9 +27,9 @@ "properties": { "title": "fableETS", "description": "All forecasts for the Daily_Bloom_binary variable for the fableETS model. Information for the model is provided as follows: fable package exponential smoothing model fable::ETS().\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableETS.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/fableNNETAR.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/fableNNETAR.json index 44876289d..20506b64b 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/fableNNETAR.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/fableNNETAR.json @@ -27,9 +27,9 @@ "properties": { "title": "fableNNETAR", "description": "All forecasts for the Daily_Bloom_binary variable for the fableNNETAR model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableNNETAR.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/glm_aed_v1.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/glm_aed_v1.json index 1b6eb31ce..cd9678da3 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/glm_aed_v1.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/glm_aed_v1.json @@ -23,7 +23,7 @@ "properties": { "title": "glm_aed_v1", "description": "All forecasts for the Daily_Bloom_binary variable for the glm_aed_v1 model. Information for the model is provided as follows: GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters..\n The model predicts this variable at the following sites: fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-20T00:00:00Z", "end_datetime": "2024-10-05T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/historic_mean.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/historic_mean.json index 8c7fd4abf..a41ddf6d7 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/historic_mean.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/historic_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "historic_mean", "description": "All forecasts for the Daily_Bloom_binary variable for the historic_mean model. Information for the model is provided as follows: Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model..\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-09T00:00:00Z", - "end_datetime": "2024-10-08T00:00:00Z", + "end_datetime": "2024-10-09T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/monthly_mean.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/monthly_mean.json index 1e293acc1..c9ff50ab5 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/monthly_mean.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/monthly_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "monthly_mean", "description": "All forecasts for the Daily_Bloom_binary variable for the monthly_mean model. Information for the model is provided as follows: This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast..\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-09T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/persistenceRW.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/persistenceRW.json index 10d4e8a20..b40b4a33e 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/persistenceRW.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Bloom_binary/models/persistenceRW.json @@ -27,9 +27,9 @@ "properties": { "title": "persistenceRW", "description": "All forecasts for the Daily_Bloom_binary variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-09T00:00:00Z", - "end_datetime": "2024-10-08T00:00:00Z", + "end_datetime": "2024-10-09T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/collection.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/collection.json index 747d51334..3cc06a966 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/collection.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/collection.json @@ -11,77 +11,77 @@ { "rel": "item", "type": "application/json", - "href": "./models/asl.auto.arima.json" + "href": "./models/asl.tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.ets.json" + "href": "./models/asl.temp.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.step.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.json" + "href": "./models/fableARIMA.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.tbats.json" + "href": "./models/fableETS.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.temp.lm.json" + "href": "./models/fableNNETAR.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/glm_aed_v1.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableARIMA.json" + "href": "./models/asl.auto.arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableETS.json" + "href": "./models/asl.climate.window.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableNNETAR.json" + "href": "./models/asl.ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/glm_aed_v1.json" + "href": "./models/asl.met.lm.step.json" }, { "rel": "item", "type": "application/json", - "href": "./models/historic_mean.json" + "href": "./models/asl.met.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/monthly_mean.json" + "href": "./models/historic_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/monthly_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.climate.window.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", @@ -136,7 +136,7 @@ "interval": [ [ "2023-10-01T00:00:00Z", - "2024-10-10T00:00:00Z" + "2024-10-11T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.auto.arima.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.auto.arima.json index a6cb6ad1c..bf74ef46f 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.auto.arima.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.auto.arima.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.auto.arima", "description": "All forecasts for the Daily_Chlorophyll-a variable for the asl.auto.arima model. Information for the model is provided as follows: forecast::auto.arima() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.climate.window.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.climate.window.json index 2dd5755fa..053051cc2 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.climate.window.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.climate.window.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.climate.window", "description": "All forecasts for the Daily_Chlorophyll-a variable for the asl.climate.window model. Information for the model is provided as follows: Climatology, but with a rolling 10-day window for historical data. This works really well for my methane forecasts at SERC, so I thought it might be useful here.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-09-04T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.ets.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.ets.json index d60ab2d70..f8661571c 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.ets.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.ets.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.ets", "description": "All forecasts for the Daily_Chlorophyll-a variable for the asl.ets model. Information for the model is provided as follows: forecast::ets() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.met.lm.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.met.lm.json index 507982176..d47865abd 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.met.lm.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.met.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm", "description": "All forecasts for the Daily_Chlorophyll-a variable for the asl.met.lm model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.met.lm.step.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.met.lm.step.json index 04e8d5a0d..ac5644a36 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.met.lm.step.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.met.lm.step.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm.step", "description": "All forecasts for the Daily_Chlorophyll-a variable for the asl.met.lm.step model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-19T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.persistence.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.persistence.json index ef033d8ab..128735115 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.persistence.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.persistence.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.persistence", "description": "All forecasts for the Daily_Chlorophyll-a variable for the asl.persistence model. Information for the model is provided as follows: The random walk persistence model has massive uncertainty at long horizons, but you could instead re-fit the persistence model for every forecast horizon to get a more precise forecast (i.e., not dynamic). I did this for my work at SERC, and am submitting here in case it is helpful.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-09-05T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.tbats.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.tbats.json index d6eae636b..d8be40bdf 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.tbats.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.tbats.json @@ -14,22 +14,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -79.8159, - 37.3129 - ], [ -79.8372, 37.3032 + ], + [ + -79.8159, + 37.3129 ] ] }, "properties": { "title": "asl.tbats", - "description": "All forecasts for the Daily_Chlorophyll-a variable for the asl.tbats model. Information for the model is provided as follows: forecast::tbats() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_Chlorophyll-a variable for the asl.tbats model. Information for the model is provided as follows: forecast::tbats() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-20T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", @@ -58,8 +58,8 @@ "Chla_ugL_mean", "Daily", "P1D", - "bvre", "fcre", + "bvre", "empirical" ], "table:columns": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.temp.lm.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.temp.lm.json index 311cf3a78..b3b316cb7 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.temp.lm.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/asl.temp.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.temp.lm", "description": "All forecasts for the Daily_Chlorophyll-a variable for the asl.temp.lm model. Information for the model is provided as follows: Linear regression with air temperature.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/climatology.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/climatology.json index 1b3bcfe99..d14b81a8f 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/climatology.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/climatology.json @@ -27,9 +27,9 @@ "properties": { "title": "climatology", "description": "All forecasts for the Daily_Chlorophyll-a variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-02T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/fableARIMA.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/fableARIMA.json index a3f553417..ff0422c3e 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/fableARIMA.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/fableARIMA.json @@ -27,9 +27,9 @@ "properties": { "title": "fableARIMA", "description": "All forecasts for the Daily_Chlorophyll-a variable for the fableARIMA model. Information for the model is provided as follows: ARIMA fit using the ARIMA() function in the fable R package.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableARIMA.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/fableETS.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/fableETS.json index 9d9fb6837..fbdddfee2 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/fableETS.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/fableETS.json @@ -27,9 +27,9 @@ "properties": { "title": "fableETS", "description": "All forecasts for the Daily_Chlorophyll-a variable for the fableETS model. Information for the model is provided as follows: fable package exponential smoothing model fable::ETS().\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableETS.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/fableNNETAR.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/fableNNETAR.json index afee23bf3..545c716a6 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/fableNNETAR.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/fableNNETAR.json @@ -14,22 +14,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -79.8372, - 37.3032 - ], [ -79.8159, 37.3129 + ], + [ + -79.8372, + 37.3032 ] ] }, "properties": { "title": "fableNNETAR", - "description": "All forecasts for the Daily_Chlorophyll-a variable for the fableNNETAR model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_Chlorophyll-a variable for the fableNNETAR model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableNNETAR.R", @@ -58,8 +58,8 @@ "Chla_ugL_mean", "Daily", "P1D", - "fcre", "bvre", + "fcre", "machine learning" ], "table:columns": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/glm_aed_v1.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/glm_aed_v1.json index 0782c23c3..f3b844e73 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/glm_aed_v1.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/glm_aed_v1.json @@ -23,7 +23,7 @@ "properties": { "title": "glm_aed_v1", "description": "All forecasts for the Daily_Chlorophyll-a variable for the glm_aed_v1 model. Information for the model is provided as follows: GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters..\n The model predicts this variable at the following sites: fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-14T00:00:00Z", "end_datetime": "2024-10-05T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/historic_mean.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/historic_mean.json index da6359d0f..da4bc9496 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/historic_mean.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/historic_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "historic_mean", "description": "All forecasts for the Daily_Chlorophyll-a variable for the historic_mean model. Information for the model is provided as follows: Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model..\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-08T00:00:00Z", + "end_datetime": "2024-10-09T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/monthly_mean.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/monthly_mean.json index 1d3b7119b..84bac336e 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/monthly_mean.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/monthly_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "monthly_mean", "description": "All forecasts for the Daily_Chlorophyll-a variable for the monthly_mean model. Information for the model is provided as follows: This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast..\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/persistenceRW.json b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/persistenceRW.json index 1bc0988ac..e9961e00f 100644 --- a/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/persistenceRW.json +++ b/data/challenge/vera4cast-stac/forecasts/Biological/Daily_Chlorophyll-a/models/persistenceRW.json @@ -27,9 +27,9 @@ "properties": { "title": "persistenceRW", "description": "All forecasts for the Daily_Chlorophyll-a variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-10-08T00:00:00Z", + "end_datetime": "2024-10-09T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/collection.json b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/collection.json index cc9ab7ef9..007c2b181 100644 --- a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/collection.json +++ b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/collection.json @@ -13,6 +13,11 @@ "type": "application/json", "href": "./models/asl.auto.arima.json" }, + { + "rel": "item", + "type": "application/json", + "href": "./models/asl.climate.window.json" + }, { "rel": "item", "type": "application/json", @@ -73,11 +78,6 @@ "type": "application/json", "href": "./models/persistenceRW.json" }, - { - "rel": "item", - "type": "application/json", - "href": "./models/asl.climate.window.json" - }, { "rel": "item", "type": "application/json", @@ -131,7 +131,7 @@ "interval": [ [ "2023-10-14T00:00:00Z", - "2024-10-10T00:00:00Z" + "2024-10-11T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.auto.arima.json b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.auto.arima.json index f91196a90..ef4e81a98 100644 --- a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.auto.arima.json +++ b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.auto.arima.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.auto.arima", "description": "All forecasts for the Daily_oxygen_concentration variable for the asl.auto.arima model. Information for the model is provided as follows: forecast::auto.arima() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.climate.window.json b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.climate.window.json index 68f71e7c0..a67f5266e 100644 --- a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.climate.window.json +++ b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.climate.window.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.climate.window", "description": "All forecasts for the Daily_oxygen_concentration variable for the asl.climate.window model. Information for the model is provided as follows: Climatology, but with a rolling 10-day window for historical data. This works really well for my methane forecasts at SERC, so I thought it might be useful here.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-09-04T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.ets.json b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.ets.json index ae0c2c198..39387659b 100644 --- a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.ets.json +++ b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.ets.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.ets", "description": "All forecasts for the Daily_oxygen_concentration variable for the asl.ets model. Information for the model is provided as follows: forecast::ets() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.met.lm.json b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.met.lm.json index 62fc13577..a4f29a3df 100644 --- a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.met.lm.json +++ b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.met.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm", "description": "All forecasts for the Daily_oxygen_concentration variable for the asl.met.lm model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.met.lm.step.json b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.met.lm.step.json index 722fe17a2..71a7f5643 100644 --- a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.met.lm.step.json +++ b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.met.lm.step.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm.step", "description": "All forecasts for the Daily_oxygen_concentration variable for the asl.met.lm.step model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-19T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.persistence.json b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.persistence.json index ddb2ec9f0..0b80a933c 100644 --- a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.persistence.json +++ b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.persistence.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.persistence", "description": "All forecasts for the Daily_oxygen_concentration variable for the asl.persistence model. Information for the model is provided as follows: The random walk persistence model has massive uncertainty at long horizons, but you could instead re-fit the persistence model for every forecast horizon to get a more precise forecast (i.e., not dynamic). I did this for my work at SERC, and am submitting here in case it is helpful.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-09-05T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.tbats.json b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.tbats.json index a02247495..485ff1d30 100644 --- a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.tbats.json +++ b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.tbats.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.tbats", "description": "All forecasts for the Daily_oxygen_concentration variable for the asl.tbats model. Information for the model is provided as follows: forecast::tbats() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-20T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.temp.lm.json b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.temp.lm.json index 01f48414d..e1ff1291c 100644 --- a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.temp.lm.json +++ b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/asl.temp.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.temp.lm", "description": "All forecasts for the Daily_oxygen_concentration variable for the asl.temp.lm model. Information for the model is provided as follows: Linear regression with air temperature.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/climatology.json b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/climatology.json index 2230cad48..c4ac68729 100644 --- a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/climatology.json +++ b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/climatology.json @@ -27,9 +27,9 @@ "properties": { "title": "climatology", "description": "All forecasts for the Daily_oxygen_concentration variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/fableNNETAR.json b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/fableNNETAR.json index 08cf8cd5b..de71c6264 100644 --- a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/fableNNETAR.json +++ b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/fableNNETAR.json @@ -14,20 +14,20 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -79.8159, - 37.3129 - ], [ -79.8372, 37.3032 + ], + [ + -79.8159, + 37.3129 ] ] }, "properties": { "title": "fableNNETAR", - "description": "All forecasts for the Daily_oxygen_concentration variable for the fableNNETAR model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_oxygen_concentration variable for the fableNNETAR model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", "end_datetime": "2024-06-03T00:00:00Z", "providers": [ @@ -58,8 +58,8 @@ "DO_mgL_mean", "Daily", "P1D", - "bvre", "fcre", + "bvre", "machine learning" ], "table:columns": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/fableNNETAR_focal.json b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/fableNNETAR_focal.json index a8c454984..669f42c7f 100644 --- a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/fableNNETAR_focal.json +++ b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/fableNNETAR_focal.json @@ -14,22 +14,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -79.8372, - 37.3032 - ], [ -79.8159, 37.3129 + ], + [ + -79.8372, + 37.3032 ] ] }, "properties": { "title": "fableNNETAR_focal", - "description": "All forecasts for the Daily_oxygen_concentration variable for the fableNNETAR_focal model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_oxygen_concentration variable for the fableNNETAR_focal model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/addelany/vera4casts/blob/main/code/combined_workflow/nnetar_workflow.R", @@ -58,8 +58,8 @@ "DO_mgL_mean", "Daily", "P1D", - "fcre", "bvre", + "fcre", "machine learning" ], "table:columns": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/glm_aed_v1.json b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/glm_aed_v1.json index ac12e9ead..360818754 100644 --- a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/glm_aed_v1.json +++ b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/glm_aed_v1.json @@ -23,7 +23,7 @@ "properties": { "title": "glm_aed_v1", "description": "All forecasts for the Daily_oxygen_concentration variable for the glm_aed_v1 model. Information for the model is provided as follows: GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters..\n The model predicts this variable at the following sites: fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-14T00:00:00Z", "end_datetime": "2024-10-05T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/historic_mean.json b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/historic_mean.json index 35e303793..8f40efe24 100644 --- a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/historic_mean.json +++ b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/historic_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "historic_mean", "description": "All forecasts for the Daily_oxygen_concentration variable for the historic_mean model. Information for the model is provided as follows: Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model..\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-08T00:00:00Z", + "end_datetime": "2024-10-09T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/monthly_mean.json b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/monthly_mean.json index 69da28d03..c8f4890e9 100644 --- a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/monthly_mean.json +++ b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/monthly_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "monthly_mean", "description": "All forecasts for the Daily_oxygen_concentration variable for the monthly_mean model. Information for the model is provided as follows: This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast..\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/persistenceRW.json b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/persistenceRW.json index cc78dc059..53944e128 100644 --- a/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/persistenceRW.json +++ b/data/challenge/vera4cast-stac/forecasts/Chemical/Daily_oxygen_concentration/models/persistenceRW.json @@ -27,9 +27,9 @@ "properties": { "title": "persistenceRW", "description": "All forecasts for the Daily_oxygen_concentration variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-08T00:00:00Z", + "end_datetime": "2024-10-09T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/collection.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/collection.json index 3ce17d301..62173fe6f 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/collection.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/collection.json @@ -81,12 +81,12 @@ { "rel": "item", "type": "application/json", - "href": "./models/asl.climate.window.json" + "href": "./models/asl.persistence.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.persistence.json" + "href": "./models/asl.climate.window.json" }, { "rel": "parent", @@ -136,7 +136,7 @@ "interval": [ [ "2023-10-14T00:00:00Z", - "2024-10-10T00:00:00Z" + "2024-10-11T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.auto.arima.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.auto.arima.json index 47235df4f..425d45a35 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.auto.arima.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.auto.arima.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.auto.arima", "description": "All forecasts for the Daily_Secchi variable for the asl.auto.arima model. Information for the model is provided as follows: forecast::auto.arima() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.climate.window.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.climate.window.json index 8bbdaebe1..3fba50a85 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.climate.window.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.climate.window.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.climate.window", "description": "All forecasts for the Daily_Secchi variable for the asl.climate.window model. Information for the model is provided as follows: Climatology, but with a rolling 10-day window for historical data. This works really well for my methane forecasts at SERC, so I thought it might be useful here.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-09-04T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.ets.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.ets.json index 51489a746..fdf1e5a6b 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.ets.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.ets.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.ets", "description": "All forecasts for the Daily_Secchi variable for the asl.ets model. Information for the model is provided as follows: forecast::ets() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.met.lm.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.met.lm.json index 60f4ad9b7..c87062f96 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.met.lm.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.met.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm", "description": "All forecasts for the Daily_Secchi variable for the asl.met.lm model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.met.lm.step.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.met.lm.step.json index 090237102..bcac8d5aa 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.met.lm.step.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.met.lm.step.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm.step", "description": "All forecasts for the Daily_Secchi variable for the asl.met.lm.step model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-19T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.persistence.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.persistence.json index 880a1d9d3..df8e5069d 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.persistence.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.persistence.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.persistence", "description": "All forecasts for the Daily_Secchi variable for the asl.persistence model. Information for the model is provided as follows: The random walk persistence model has massive uncertainty at long horizons, but you could instead re-fit the persistence model for every forecast horizon to get a more precise forecast (i.e., not dynamic). I did this for my work at SERC, and am submitting here in case it is helpful.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-09-05T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.tbats.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.tbats.json index dc39fe3dc..ca34cc91c 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.tbats.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.tbats.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.tbats", "description": "All forecasts for the Daily_Secchi variable for the asl.tbats model. Information for the model is provided as follows: forecast::tbats() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-20T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.temp.lm.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.temp.lm.json index c99c1dc76..668bd39db 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.temp.lm.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/asl.temp.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.temp.lm", "description": "All forecasts for the Daily_Secchi variable for the asl.temp.lm model. Information for the model is provided as follows: Linear regression with air temperature.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/climatology.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/climatology.json index 1cc3e7366..bcec3798a 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/climatology.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/climatology.json @@ -27,9 +27,9 @@ "properties": { "title": "climatology", "description": "All forecasts for the Daily_Secchi variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/fableNNETAR.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/fableNNETAR.json index 4d9efc90d..bf7a37f6c 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/fableNNETAR.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/fableNNETAR.json @@ -27,7 +27,7 @@ "properties": { "title": "fableNNETAR", "description": "All forecasts for the Daily_Secchi variable for the fableNNETAR model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", "end_datetime": "2024-06-03T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/fableNNETAR_focal.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/fableNNETAR_focal.json index 276cdc9a3..204817146 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/fableNNETAR_focal.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/fableNNETAR_focal.json @@ -14,22 +14,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -79.8372, - 37.3032 - ], [ -79.8159, 37.3129 + ], + [ + -79.8372, + 37.3032 ] ] }, "properties": { "title": "fableNNETAR_focal", - "description": "All forecasts for the Daily_Secchi variable for the fableNNETAR_focal model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_Secchi variable for the fableNNETAR_focal model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/addelany/vera4casts/blob/main/code/combined_workflow/nnetar_workflow.R", @@ -58,8 +58,8 @@ "Secchi_m_sample", "Daily", "P1D", - "fcre", "bvre", + "fcre", "machine learning" ], "table:columns": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/glm_aed_v1.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/glm_aed_v1.json index 598ae290b..22de58dee 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/glm_aed_v1.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/glm_aed_v1.json @@ -23,7 +23,7 @@ "properties": { "title": "glm_aed_v1", "description": "All forecasts for the Daily_Secchi variable for the glm_aed_v1 model. Information for the model is provided as follows: GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters..\n The model predicts this variable at the following sites: fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-14T00:00:00Z", "end_datetime": "2024-10-05T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/historic_mean.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/historic_mean.json index 186a771b0..6fd37313c 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/historic_mean.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/historic_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "historic_mean", "description": "All forecasts for the Daily_Secchi variable for the historic_mean model. Information for the model is provided as follows: Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model..\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-08T00:00:00Z", + "end_datetime": "2024-10-09T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/monthly_mean.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/monthly_mean.json index 6238c379d..54e454eac 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/monthly_mean.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/monthly_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "monthly_mean", "description": "All forecasts for the Daily_Secchi variable for the monthly_mean model. Information for the model is provided as follows: This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast..\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/persistenceRW.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/persistenceRW.json index 8978b05ba..0033f8166 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/persistenceRW.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/persistenceRW.json @@ -27,9 +27,9 @@ "properties": { "title": "persistenceRW", "description": "All forecasts for the Daily_Secchi variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-08T00:00:00Z", + "end_datetime": "2024-10-09T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/secchi_last3obs_mean.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/secchi_last3obs_mean.json index d329b08be..d3b8b738b 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/secchi_last3obs_mean.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Secchi/models/secchi_last3obs_mean.json @@ -23,7 +23,7 @@ "properties": { "title": "secchi_last3obs_mean", "description": "All forecasts for the Daily_Secchi variable for the secchi_last3obs_mean model. Information for the model is provided as follows: This forecast simply takes the mean of the last three secchi observations and uses the standard deviation of that mean for the uncertainty around the forecast..\n The model predicts this variable at the following sites: fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-02T00:00:00Z", "end_datetime": "2024-09-20T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/collection.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/collection.json index e1326f163..ab865ea72 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/collection.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/collection.json @@ -11,97 +11,97 @@ { "rel": "item", "type": "application/json", - "href": "./models/asl.auto.arima.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.climate.window.json" + "href": "./models/fableNNETAR.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.ets.json" + "href": "./models/fableNNETAR_focal.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.step.json" + "href": "./models/flareGOTM.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.json" + "href": "./models/flareSimstrat.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.persistence.json" + "href": "./models/glm_aed_v1.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.tbats.json" + "href": "./models/historic_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.temp.lm.json" + "href": "./models/inflow_gefsClimAED.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/monthly_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableNNETAR.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableNNETAR_focal.json" + "href": "./models/asl.auto.arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flareGOTM.json" + "href": "./models/asl.ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flareSimstrat.json" + "href": "./models/asl.met.lm.step.json" }, { "rel": "item", "type": "application/json", - "href": "./models/glm_aed_v1.json" + "href": "./models/asl.met.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/historic_mean.json" + "href": "./models/asl.persistence.json" }, { "rel": "item", "type": "application/json", - "href": "./models/inflow_gefsClimAED.json" + "href": "./models/asl.tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/monthly_mean.json" + "href": "./models/asl.temp.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceFO.json" + "href": "./models/asl.climate.window.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/persistenceFO.json" }, { "rel": "parent", @@ -151,7 +151,7 @@ "interval": [ [ "2023-09-21T00:00:00Z", - "2024-10-10T00:00:00Z" + "2024-10-11T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.auto.arima.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.auto.arima.json index e3eb10d09..51b7646e4 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.auto.arima.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.auto.arima.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.auto.arima", "description": "All forecasts for the Daily_Water_temperature variable for the asl.auto.arima model. Information for the model is provided as follows: forecast::auto.arima() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.climate.window.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.climate.window.json index 823d86ca6..0b1217fd1 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.climate.window.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.climate.window.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.climate.window", "description": "All forecasts for the Daily_Water_temperature variable for the asl.climate.window model. Information for the model is provided as follows: Climatology, but with a rolling 10-day window for historical data. This works really well for my methane forecasts at SERC, so I thought it might be useful here.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-09-04T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.ets.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.ets.json index 5f5752284..d47f2958c 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.ets.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.ets.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.ets", "description": "All forecasts for the Daily_Water_temperature variable for the asl.ets model. Information for the model is provided as follows: forecast::ets() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.met.lm.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.met.lm.json index 215b9d488..32b860590 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.met.lm.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.met.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm", "description": "All forecasts for the Daily_Water_temperature variable for the asl.met.lm model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.met.lm.step.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.met.lm.step.json index 96d0f09af..8ddfaa08f 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.met.lm.step.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.met.lm.step.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm.step", "description": "All forecasts for the Daily_Water_temperature variable for the asl.met.lm.step model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-19T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.persistence.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.persistence.json index 7a709db0c..77bf4e9e8 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.persistence.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.persistence.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.persistence", "description": "All forecasts for the Daily_Water_temperature variable for the asl.persistence model. Information for the model is provided as follows: The random walk persistence model has massive uncertainty at long horizons, but you could instead re-fit the persistence model for every forecast horizon to get a more precise forecast (i.e., not dynamic). I did this for my work at SERC, and am submitting here in case it is helpful.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-09-05T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.tbats.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.tbats.json index 902f4ef45..6c58475ae 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.tbats.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.tbats.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.tbats", "description": "All forecasts for the Daily_Water_temperature variable for the asl.tbats model. Information for the model is provided as follows: forecast::tbats() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-20T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.temp.lm.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.temp.lm.json index 2cc9353ec..1188f0016 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.temp.lm.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/asl.temp.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.temp.lm", "description": "All forecasts for the Daily_Water_temperature variable for the asl.temp.lm model. Information for the model is provided as follows: Linear regression with air temperature.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/climatology.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/climatology.json index 182a72d35..3174de185 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/climatology.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/climatology.json @@ -14,6 +14,10 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -79.8357, + 37.3078 + ], [ -79.8159, 37.3129 @@ -21,19 +25,15 @@ [ -79.8372, 37.3032 - ], - [ - -79.8357, - 37.3078 ] ] }, "properties": { "title": "climatology", - "description": "All forecasts for the Daily_Water_temperature variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: bvre, fcre, tubr.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_Water_temperature variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: tubr, bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-09-21T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", @@ -62,9 +62,9 @@ "Temp_C_mean", "Daily", "P1D", + "tubr", "bvre", "fcre", - "tubr", "empirical" ], "table:columns": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/fableNNETAR.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/fableNNETAR.json index 5e8f7b83d..725615f86 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/fableNNETAR.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/fableNNETAR.json @@ -14,20 +14,20 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -79.8372, - 37.3032 - ], [ -79.8159, 37.3129 + ], + [ + -79.8372, + 37.3032 ] ] }, "properties": { "title": "fableNNETAR", - "description": "All forecasts for the Daily_Water_temperature variable for the fableNNETAR model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All forecasts for the Daily_Water_temperature variable for the fableNNETAR model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", "end_datetime": "2024-06-03T00:00:00Z", "providers": [ @@ -58,8 +58,8 @@ "Temp_C_mean", "Daily", "P1D", - "fcre", "bvre", + "fcre", "machine learning" ], "table:columns": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/fableNNETAR_focal.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/fableNNETAR_focal.json index ffc609317..0907ebeba 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/fableNNETAR_focal.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/fableNNETAR_focal.json @@ -27,9 +27,9 @@ "properties": { "title": "fableNNETAR_focal", "description": "All forecasts for the Daily_Water_temperature variable for the fableNNETAR_focal model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/addelany/vera4casts/blob/main/code/combined_workflow/nnetar_workflow.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/flareGOTM.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/flareGOTM.json index e4e9a4087..f02811817 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/flareGOTM.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/flareGOTM.json @@ -23,9 +23,9 @@ "properties": { "title": "flareGOTM", "description": "All forecasts for the Daily_Water_temperature variable for the flareGOTM model. Information for the model is provided as follows: FLARE-GOTM combines the 1D hydrodynamic process-based model GOTM, a data assimilation algorithm, and NOAA weather data to forecast water column temperatures..\n The model predicts this variable at the following sites: fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-01-21T00:00:00Z", - "end_datetime": "2024-10-04T00:00:00Z", + "end_datetime": "2024-10-05T00:00:00Z", "providers": [ { "url": null, diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/flareSimstrat.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/flareSimstrat.json index 8c30f73be..f41992d68 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/flareSimstrat.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/flareSimstrat.json @@ -23,9 +23,9 @@ "properties": { "title": "flareSimstrat", "description": "All forecasts for the Daily_Water_temperature variable for the flareSimstrat model. Information for the model is provided as follows: FLARE-Simstrat combines the 1D process-based model Simstrat, a data assimilation algorithm (EnKF) and NOAA driver weather data to make predictions of water column temperatures..\n The model predicts this variable at the following sites: fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-01-21T00:00:00Z", - "end_datetime": "2024-10-04T00:00:00Z", + "end_datetime": "2024-10-05T00:00:00Z", "providers": [ { "url": "https://github.com/FLARE-forecast/FCRE-forecast-code/blob/main/workflows/ler/combined_workflow_Simstrat.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/glm_aed_v1.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/glm_aed_v1.json index a9aa7e65a..2d0f0390c 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/glm_aed_v1.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/glm_aed_v1.json @@ -23,7 +23,7 @@ "properties": { "title": "glm_aed_v1", "description": "All forecasts for the Daily_Water_temperature variable for the glm_aed_v1 model. Information for the model is provided as follows: GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters..\n The model predicts this variable at the following sites: fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-14T00:00:00Z", "end_datetime": "2024-10-05T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/historic_mean.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/historic_mean.json index e7c75472e..b03c0cdc3 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/historic_mean.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/historic_mean.json @@ -31,9 +31,9 @@ "properties": { "title": "historic_mean", "description": "All forecasts for the Daily_Water_temperature variable for the historic_mean model. Information for the model is provided as follows: Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model..\n The model predicts this variable at the following sites: tubr, fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-08T00:00:00Z", + "end_datetime": "2024-10-09T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/inflow_gefsClimAED.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/inflow_gefsClimAED.json index c3e9cc704..f12be0d99 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/inflow_gefsClimAED.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/inflow_gefsClimAED.json @@ -23,9 +23,9 @@ "properties": { "title": "inflow_gefsClimAED", "description": "All forecasts for the Daily_Water_temperature variable for the inflow_gefsClimAED model. Information for the model is provided as follows: flow is forecasted as using a linear relationship between historical flow, month, and 5-day sum of precipitation. Temperature is forecasted using a linear relationship between historical water temperature, month, and 5-day mean air temperature. NOAA GEFS is then used to get the future values of 5-day sum precipitation and mean temperature. Nutrients are forecasting using the DOY climatology. The DOY climatology was developed using a linear interpolation of the historical samples..\n The model predicts this variable at the following sites: tubr.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-13T00:00:00Z", - "end_datetime": "2024-10-06T00:00:00Z", + "end_datetime": "2024-10-07T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast_models/blob/main/inflow_aed.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/monthly_mean.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/monthly_mean.json index 1868ba4ba..ba5d1c8de 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/monthly_mean.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/monthly_mean.json @@ -31,9 +31,9 @@ "properties": { "title": "monthly_mean", "description": "All forecasts for the Daily_Water_temperature variable for the monthly_mean model. Information for the model is provided as follows: This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast..\n The model predicts this variable at the following sites: tubr, fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/persistenceFO.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/persistenceFO.json index 399611b9b..3f0f33d85 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/persistenceFO.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/persistenceFO.json @@ -23,7 +23,7 @@ "properties": { "title": "persistenceFO", "description": "All forecasts for the Daily_Water_temperature variable for the persistenceFO model. Information for the model is provided as follows: another persistence forecast.\n The model predicts this variable at the following sites: bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-09-27T00:00:00Z", "end_datetime": "2023-10-30T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/persistenceRW.json b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/persistenceRW.json index 8a615cc22..8569aab6a 100644 --- a/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/persistenceRW.json +++ b/data/challenge/vera4cast-stac/forecasts/Physical/Daily_Water_temperature/models/persistenceRW.json @@ -31,9 +31,9 @@ "properties": { "title": "persistenceRW", "description": "All forecasts for the Daily_Water_temperature variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: bvre, fcre, tubr.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-09-21T00:00:00Z", - "end_datetime": "2024-10-08T00:00:00Z", + "end_datetime": "2024-10-09T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/inventory/collection.json b/data/challenge/vera4cast-stac/inventory/collection.json index c2db5f018..93a816d03 100644 --- a/data/challenge/vera4cast-stac/inventory/collection.json +++ b/data/challenge/vera4cast-stac/inventory/collection.json @@ -58,7 +58,7 @@ "interval": [ [ "2023-09-21T00:00:00Z", - "2024-10-10T00:00:00Z" + "2024-10-11T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/noaa_forecasts/Pseudo/collection.json b/data/challenge/vera4cast-stac/noaa_forecasts/Pseudo/collection.json index 190907ba6..55824be5c 100644 --- a/data/challenge/vera4cast-stac/noaa_forecasts/Pseudo/collection.json +++ b/data/challenge/vera4cast-stac/noaa_forecasts/Pseudo/collection.json @@ -58,7 +58,7 @@ "interval": [ [ "2020-01-01T00:00:00Z", - "2024-09-05T00:00:00Z" + "2024-09-06T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/noaa_forecasts/Stage1-stats/collection.json b/data/challenge/vera4cast-stac/noaa_forecasts/Stage1-stats/collection.json index 8af523355..568165566 100644 --- a/data/challenge/vera4cast-stac/noaa_forecasts/Stage1-stats/collection.json +++ b/data/challenge/vera4cast-stac/noaa_forecasts/Stage1-stats/collection.json @@ -58,7 +58,7 @@ "interval": [ [ "2020-01-01T00:00:00Z", - "2024-09-05T00:00:00Z" + "2024-09-06T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/noaa_forecasts/Stage1/collection.json b/data/challenge/vera4cast-stac/noaa_forecasts/Stage1/collection.json index 5bfcb6687..bb230fdbb 100644 --- a/data/challenge/vera4cast-stac/noaa_forecasts/Stage1/collection.json +++ b/data/challenge/vera4cast-stac/noaa_forecasts/Stage1/collection.json @@ -58,7 +58,7 @@ "interval": [ [ "2020-01-01T00:00:00Z", - "2024-09-05T00:00:00Z" + "2024-09-06T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/noaa_forecasts/Stage2/collection.json b/data/challenge/vera4cast-stac/noaa_forecasts/Stage2/collection.json index 5328c5aeb..e8db2a3ab 100644 --- a/data/challenge/vera4cast-stac/noaa_forecasts/Stage2/collection.json +++ b/data/challenge/vera4cast-stac/noaa_forecasts/Stage2/collection.json @@ -58,7 +58,7 @@ "interval": [ [ "2020-01-01T00:00:00Z", - "2024-09-05T00:00:00Z" + "2024-09-06T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/noaa_forecasts/Stage3/collection.json b/data/challenge/vera4cast-stac/noaa_forecasts/Stage3/collection.json index 1a3dec4b9..9ce548819 100644 --- a/data/challenge/vera4cast-stac/noaa_forecasts/Stage3/collection.json +++ b/data/challenge/vera4cast-stac/noaa_forecasts/Stage3/collection.json @@ -58,7 +58,7 @@ "interval": [ [ "2020-01-01T00:00:00Z", - "2024-09-05T00:00:00Z" + "2024-09-06T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/noaa_forecasts/collection.json b/data/challenge/vera4cast-stac/noaa_forecasts/collection.json index b32eb8c55..26b9b472c 100644 --- a/data/challenge/vera4cast-stac/noaa_forecasts/collection.json +++ b/data/challenge/vera4cast-stac/noaa_forecasts/collection.json @@ -86,7 +86,7 @@ "interval": [ [ "2020-01-01T00:00:00Z", - "2024-09-05T00:00:00Z" + "2024-09-06T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/collection.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/collection.json index ab2033f97..68c8c18ab 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/collection.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/collection.json @@ -11,73 +11,78 @@ { "rel": "item", "type": "application/json", - "href": "./models/fableNNETAR.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/historic_mean.json" + "href": "./models/glm_aed_v1.json" }, { "rel": "item", "type": "application/json", - "href": "./models/glm_aed_v1.json" + "href": "./models/fableETS.json" }, { "rel": "item", "type": "application/json", - "href": "./models/monthly_mean.json" + "href": "./models/fableNNETAR.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/historic_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.ets.json" + "href": "./models/monthly_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.tbats.json" + "href": "./models/asl.auto.arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/asl.met.lm.step.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.temp.lm.json" + "href": "./models/asl.met.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableETS.json" + "href": "./models/asl.temp.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.auto.arima.json" + "href": "./models/asl.tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.step.json" + "href": "./models/asl.ets.json" }, { "rel": "item", "type": "application/json", "href": "./models/fableARIMA.json" }, + { + "rel": "item", + "type": "application/json", + "href": "./models/asl.climate.window.json" + }, { "rel": "parent", "type": "application/json", @@ -126,7 +131,7 @@ "interval": [ [ "2023-10-01T00:00:00Z", - "2024-09-03T00:00:00Z" + "2024-09-04T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.auto.arima.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.auto.arima.json index b5c7fcc0a..d608e2f7e 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.auto.arima.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.auto.arima.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.auto.arima", "description": "All scores for the Daily_Bloom_binary variable for the asl.auto.arima model. Information for the model is provided as follows: forecast::auto.arima() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-04-29T00:00:00Z", "end_datetime": "2024-06-17T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.climate.window.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.climate.window.json new file mode 100644 index 000000000..2137f6fdc --- /dev/null +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.climate.window.json @@ -0,0 +1,233 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "asl.climate.window_Bloom_binary_mean_P1D_scores", + "bbox": [ + -79.8372, + 37.3032, + -79.8159, + 37.3129 + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [ + -79.8159, + 37.3129 + ], + [ + -79.8372, + 37.3032 + ] + ] + }, + "properties": { + "title": "asl.climate.window", + "description": "All scores for the Daily_Bloom_binary variable for the asl.climate.window model. Information for the model is provided as follows: Climatology, but with a rolling 10-day window for historical data. This works really well for my methane forecasts at SERC, so I thought it might be useful here.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-06T00:00:00Z", + "start_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", + "providers": [ + { + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "http://ecoforecast.centers.vt.edu/", + "name": "Virginia Tech Center for Ecosystem Forecasting", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Scores", + "vera4cast", + "Biological", + "asl.climate.window", + "Bloom_binary", + "Bloom_binary_mean", + "Daily", + "P1D", + "bvre", + "fcre", + "empirical" + ], + "table:columns": [ + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." + }, + { + "name": "datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: \u201censemble.\u201d Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The \u201csample\u201d distribution is synonymous with \u201censemble.\u201d For summary statistics: \u201csummary.\u201dIf this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "observation", + "type": "double", + "description": "observed value for variable" + }, + { + "name": "crps", + "type": "double", + "description": "crps forecast score" + }, + { + "name": "logs", + "type": "double", + "description": "logs forecast score" + }, + { + "name": "mean", + "type": "double", + "description": "mean forecast prediction" + }, + { + "name": "median", + "type": "double", + "description": "median forecast prediction" + }, + { + "name": "sd", + "type": "double", + "description": "standard deviation forecasts" + }, + { + "name": "quantile97.5", + "type": "double", + "description": "upper 97.5 percentile value of forecast" + }, + { + "name": "quantile02.5", + "type": "double", + "description": "upper 2.5 percentile value of forecast" + }, + { + "name": "quantile90", + "type": "double", + "description": "upper 90 percentile value of forecast" + }, + { + "name": "quantile10", + "type": "double", + "description": "upper 10 percentile value of forecast" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + }, + { + "name": "project_id", + "type": "string", + "description": "unique project identifier" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + } + ] + }, + "collection": "scores", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "asl.climate.window" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "asl.climate.window" + }, + { + "rel": "self", + "href": "asl.climate.window.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "type": "text/html", + "title": "Link for Model Code" + }, + { + "rel": "item", + "href": "https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/LTREB-reservoirs/vera4cast/main/catalog/scores/Biological/Daily_Bloom_binary/models/asl.climate.window.json", + "type": "text/html", + "title": "Link for rendered STAC item" + }, + { + "rel": "item", + "href": "https://raw.githubusercontent.com/LTREB-reservoirs/vera4cast/main/catalog/scores/Biological/Daily_Bloom_binary/models/asl.climate.window.json", + "type": "text/html", + "title": "Link for raw JSON file" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.climate.window.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.climate.window.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Bloom_binary", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/bundled-parquetproject_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=asl.climate.window?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/bundled-parquetproject_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=asl.climate.window?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} \ No newline at end of file diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.ets.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.ets.json index 2aa46e890..e421d1c90 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.ets.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.ets.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.ets", "description": "All scores for the Daily_Bloom_binary variable for the asl.ets model. Information for the model is provided as follows: forecast::ets() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-04-29T00:00:00Z", "end_datetime": "2024-06-17T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.met.lm.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.met.lm.json index 2adbb9736..be43181ef 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.met.lm.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.met.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm", "description": "All scores for the Daily_Bloom_binary variable for the asl.met.lm model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-14T00:00:00Z", "end_datetime": "2024-06-16T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.met.lm.step.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.met.lm.step.json index ed31fd632..45ab5c601 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.met.lm.step.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.met.lm.step.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm.step", "description": "All scores for the Daily_Bloom_binary variable for the asl.met.lm.step model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-14T00:00:00Z", "end_datetime": "2024-06-16T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.tbats.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.tbats.json index 69c380f0f..18789deb5 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.tbats.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.tbats.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.tbats", "description": "All scores for the Daily_Bloom_binary variable for the asl.tbats model. Information for the model is provided as follows: forecast::tbats() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-04-29T00:00:00Z", "end_datetime": "2024-06-16T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.temp.lm.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.temp.lm.json index 2a1863bc9..4673e62ad 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.temp.lm.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/asl.temp.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.temp.lm", "description": "All scores for the Daily_Bloom_binary variable for the asl.temp.lm model. Information for the model is provided as follows: Linear regression with air temperature.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-14T00:00:00Z", "end_datetime": "2024-06-16T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/climatology.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/climatology.json index 457e11f0f..2618efdef 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/climatology.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/climatology.json @@ -27,9 +27,9 @@ "properties": { "title": "climatology", "description": "All scores for the Daily_Bloom_binary variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-09T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/fableARIMA.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/fableARIMA.json index a5132ebf8..6da6aa714 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/fableARIMA.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/fableARIMA.json @@ -27,9 +27,9 @@ "properties": { "title": "fableARIMA", "description": "All scores for the Daily_Bloom_binary variable for the fableARIMA model. Information for the model is provided as follows: ARIMA fit using the ARIMA() function in the fable R package.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableARIMA.R", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/fableETS.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/fableETS.json index 93c9910c8..ab9ebafb8 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/fableETS.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/fableETS.json @@ -27,9 +27,9 @@ "properties": { "title": "fableETS", "description": "All scores for the Daily_Bloom_binary variable for the fableETS model. Information for the model is provided as follows: fable package exponential smoothing model fable::ETS().\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableETS.R", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/fableNNETAR.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/fableNNETAR.json index 9f5a35739..6f5fad720 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/fableNNETAR.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/fableNNETAR.json @@ -14,22 +14,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -79.8372, - 37.3032 - ], [ -79.8159, 37.3129 + ], + [ + -79.8372, + 37.3032 ] ] }, "properties": { "title": "fableNNETAR", - "description": "All scores for the Daily_Bloom_binary variable for the fableNNETAR model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package.\n The model predicts this variable at the following sites: fcre, bvre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All scores for the Daily_Bloom_binary variable for the fableNNETAR model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableNNETAR.R", @@ -58,8 +58,8 @@ "Bloom_binary_mean", "Daily", "P1D", - "fcre", "bvre", + "fcre", "machine learning" ], "table:columns": [ diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/glm_aed_v1.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/glm_aed_v1.json index 2a378a47a..9bad35e24 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/glm_aed_v1.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/glm_aed_v1.json @@ -23,9 +23,9 @@ "properties": { "title": "glm_aed_v1", "description": "All scores for the Daily_Bloom_binary variable for the glm_aed_v1 model. Information for the model is provided as follows: GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters..\n The model predicts this variable at the following sites: fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-20T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/FLARE-forecast/FCRE-forecast-code/blob/main/workflows/glm_aed/combined_run_aed.R", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/historic_mean.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/historic_mean.json index ea9b7fb91..626159fed 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/historic_mean.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/historic_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "historic_mean", "description": "All scores for the Daily_Bloom_binary variable for the historic_mean model. Information for the model is provided as follows: Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model..\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-09T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/monthly_mean.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/monthly_mean.json index fdd8413a8..1a0be3781 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/monthly_mean.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/monthly_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "monthly_mean", "description": "All scores for the Daily_Bloom_binary variable for the monthly_mean model. Information for the model is provided as follows: This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast..\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-09T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/persistenceRW.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/persistenceRW.json index 21c819622..64ec9b7a3 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/persistenceRW.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Bloom_binary/models/persistenceRW.json @@ -27,9 +27,9 @@ "properties": { "title": "persistenceRW", "description": "All scores for the Daily_Bloom_binary variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-09T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/collection.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/collection.json index 7d06ce38f..38d862b62 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/collection.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/collection.json @@ -16,12 +16,12 @@ { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.step.json" + "href": "./models/asl.ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.tbats.json" + "href": "./models/asl.met.lm.step.json" }, { "rel": "item", @@ -31,17 +31,22 @@ { "rel": "item", "type": "application/json", - "href": "./models/fableARIMA.json" + "href": "./models/asl.tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableETS.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableNNETAR.json" + "href": "./models/fableARIMA.json" + }, + { + "rel": "item", + "type": "application/json", + "href": "./models/fableETS.json" }, { "rel": "item", @@ -51,7 +56,7 @@ { "rel": "item", "type": "application/json", - "href": "./models/historic_mean.json" + "href": "./models/asl.climate.window.json" }, { "rel": "item", @@ -61,22 +66,22 @@ { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/asl.temp.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/fableNNETAR.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.temp.lm.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.ets.json" + "href": "./models/historic_mean.json" }, { "rel": "parent", @@ -126,7 +131,7 @@ "interval": [ [ "2023-10-01T00:00:00Z", - "2024-09-03T00:00:00Z" + "2024-09-04T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.auto.arima.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.auto.arima.json index 67d0653a7..b61017bbe 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.auto.arima.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.auto.arima.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.auto.arima", "description": "All scores for the Daily_Chlorophyll-a variable for the asl.auto.arima model. Information for the model is provided as follows: forecast::auto.arima() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.climate.window.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.climate.window.json new file mode 100644 index 000000000..289a0ac5a --- /dev/null +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.climate.window.json @@ -0,0 +1,233 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "asl.climate.window_Chla_ugL_mean_P1D_scores", + "bbox": [ + -79.8372, + 37.3032, + -79.8159, + 37.3129 + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [ + -79.8159, + 37.3129 + ], + [ + -79.8372, + 37.3032 + ] + ] + }, + "properties": { + "title": "asl.climate.window", + "description": "All scores for the Daily_Chlorophyll-a variable for the asl.climate.window model. Information for the model is provided as follows: Climatology, but with a rolling 10-day window for historical data. This works really well for my methane forecasts at SERC, so I thought it might be useful here.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-06T00:00:00Z", + "start_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", + "providers": [ + { + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "http://ecoforecast.centers.vt.edu/", + "name": "Virginia Tech Center for Ecosystem Forecasting", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Scores", + "vera4cast", + "Biological", + "asl.climate.window", + "Chlorophyll-a", + "Chla_ugL_mean", + "Daily", + "P1D", + "bvre", + "fcre", + "empirical" + ], + "table:columns": [ + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." + }, + { + "name": "datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: \u201censemble.\u201d Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The \u201csample\u201d distribution is synonymous with \u201censemble.\u201d For summary statistics: \u201csummary.\u201dIf this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "observation", + "type": "double", + "description": "observed value for variable" + }, + { + "name": "crps", + "type": "double", + "description": "crps forecast score" + }, + { + "name": "logs", + "type": "double", + "description": "logs forecast score" + }, + { + "name": "mean", + "type": "double", + "description": "mean forecast prediction" + }, + { + "name": "median", + "type": "double", + "description": "median forecast prediction" + }, + { + "name": "sd", + "type": "double", + "description": "standard deviation forecasts" + }, + { + "name": "quantile97.5", + "type": "double", + "description": "upper 97.5 percentile value of forecast" + }, + { + "name": "quantile02.5", + "type": "double", + "description": "upper 2.5 percentile value of forecast" + }, + { + "name": "quantile90", + "type": "double", + "description": "upper 90 percentile value of forecast" + }, + { + "name": "quantile10", + "type": "double", + "description": "upper 10 percentile value of forecast" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + }, + { + "name": "project_id", + "type": "string", + "description": "unique project identifier" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + } + ] + }, + "collection": "scores", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "asl.climate.window" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "asl.climate.window" + }, + { + "rel": "self", + "href": "asl.climate.window.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "type": "text/html", + "title": "Link for Model Code" + }, + { + "rel": "item", + "href": "https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/LTREB-reservoirs/vera4cast/main/catalog/scores/Biological/Daily_Chlorophyll-a/models/asl.climate.window.json", + "type": "text/html", + "title": "Link for rendered STAC item" + }, + { + "rel": "item", + "href": "https://raw.githubusercontent.com/LTREB-reservoirs/vera4cast/main/catalog/scores/Biological/Daily_Chlorophyll-a/models/asl.climate.window.json", + "type": "text/html", + "title": "Link for raw JSON file" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.climate.window.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.climate.window.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Chlorophyll-a", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/bundled-parquetproject_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=asl.climate.window?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/bundled-parquetproject_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=asl.climate.window?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} \ No newline at end of file diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.ets.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.ets.json index 5c15876a8..8db2346bc 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.ets.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.ets.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.ets", "description": "All scores for the Daily_Chlorophyll-a variable for the asl.ets model. Information for the model is provided as follows: forecast::ets() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.met.lm.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.met.lm.json index 531053db5..a8bb3b178 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.met.lm.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.met.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm", "description": "All scores for the Daily_Chlorophyll-a variable for the asl.met.lm model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.met.lm.step.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.met.lm.step.json index 36cfa2de6..cf98e60d9 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.met.lm.step.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.met.lm.step.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm.step", "description": "All scores for the Daily_Chlorophyll-a variable for the asl.met.lm.step model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-19T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.tbats.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.tbats.json index 8423af271..c6ad45367 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.tbats.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.tbats.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.tbats", "description": "All scores for the Daily_Chlorophyll-a variable for the asl.tbats model. Information for the model is provided as follows: forecast::tbats() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-20T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.temp.lm.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.temp.lm.json index a87fb6022..a438351d3 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.temp.lm.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/asl.temp.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.temp.lm", "description": "All scores for the Daily_Chlorophyll-a variable for the asl.temp.lm model. Information for the model is provided as follows: Linear regression with air temperature.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/climatology.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/climatology.json index 9f9cf77e8..aa059ef4d 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/climatology.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/climatology.json @@ -27,9 +27,9 @@ "properties": { "title": "climatology", "description": "All scores for the Daily_Chlorophyll-a variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-02T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/fableARIMA.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/fableARIMA.json index 1ca591df6..394e9c3e1 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/fableARIMA.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/fableARIMA.json @@ -27,9 +27,9 @@ "properties": { "title": "fableARIMA", "description": "All scores for the Daily_Chlorophyll-a variable for the fableARIMA model. Information for the model is provided as follows: ARIMA fit using the ARIMA() function in the fable R package.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableARIMA.R", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/fableETS.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/fableETS.json index c27f579fd..52d9beca2 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/fableETS.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/fableETS.json @@ -27,9 +27,9 @@ "properties": { "title": "fableETS", "description": "All scores for the Daily_Chlorophyll-a variable for the fableETS model. Information for the model is provided as follows: fable package exponential smoothing model fable::ETS().\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableETS.R", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/fableNNETAR.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/fableNNETAR.json index 221ca2a6a..435ceac9c 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/fableNNETAR.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/fableNNETAR.json @@ -27,9 +27,9 @@ "properties": { "title": "fableNNETAR", "description": "All scores for the Daily_Chlorophyll-a variable for the fableNNETAR model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableNNETAR.R", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/glm_aed_v1.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/glm_aed_v1.json index 0fe052f1c..e891b17a0 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/glm_aed_v1.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/glm_aed_v1.json @@ -23,9 +23,9 @@ "properties": { "title": "glm_aed_v1", "description": "All scores for the Daily_Chlorophyll-a variable for the glm_aed_v1 model. Information for the model is provided as follows: GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters..\n The model predicts this variable at the following sites: fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-14T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/FLARE-forecast/FCRE-forecast-code/blob/main/workflows/glm_aed/combined_run_aed.R", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/historic_mean.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/historic_mean.json index a3398df5c..6c6fecb81 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/historic_mean.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/historic_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "historic_mean", "description": "All scores for the Daily_Chlorophyll-a variable for the historic_mean model. Information for the model is provided as follows: Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model..\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/monthly_mean.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/monthly_mean.json index dff8637d5..2f8fd6a43 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/monthly_mean.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/monthly_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "monthly_mean", "description": "All scores for the Daily_Chlorophyll-a variable for the monthly_mean model. Information for the model is provided as follows: This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast..\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/persistenceRW.json b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/persistenceRW.json index b2f904973..a9bfc9cd0 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/persistenceRW.json +++ b/data/challenge/vera4cast-stac/scores/Biological/Daily_Chlorophyll-a/models/persistenceRW.json @@ -27,9 +27,9 @@ "properties": { "title": "persistenceRW", "description": "All scores for the Daily_Chlorophyll-a variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/scores/Biological/collection.json b/data/challenge/vera4cast-stac/scores/Biological/collection.json index 86b833f58..258d31a90 100644 --- a/data/challenge/vera4cast-stac/scores/Biological/collection.json +++ b/data/challenge/vera4cast-stac/scores/Biological/collection.json @@ -71,7 +71,7 @@ "interval": [ [ "2023-09-21T00:00:00Z", - "2024-09-04T00:00:00Z" + "2024-09-05T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/collection.json b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/collection.json index b719b3a47..666e7b16c 100644 --- a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/collection.json +++ b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/collection.json @@ -11,27 +11,27 @@ { "rel": "item", "type": "application/json", - "href": "./models/asl.ets.json" + "href": "./models/historic_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.auto.arima.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.step.json" + "href": "./models/asl.auto.arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.json" + "href": "./models/asl.ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.temp.lm.json" + "href": "./models/asl.met.lm.json" }, { "rel": "item", @@ -61,17 +61,22 @@ { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/asl.tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.tbats.json" + "href": "./models/asl.temp.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/historic_mean.json" + "href": "./models/asl.climate.window.json" + }, + { + "rel": "item", + "type": "application/json", + "href": "./models/asl.met.lm.step.json" }, { "rel": "parent", @@ -121,7 +126,7 @@ "interval": [ [ "2023-10-14T00:00:00Z", - "2024-09-03T00:00:00Z" + "2024-09-04T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.auto.arima.json b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.auto.arima.json index 0295587de..d3d85869d 100644 --- a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.auto.arima.json +++ b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.auto.arima.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.auto.arima", "description": "All scores for the Daily_oxygen_concentration variable for the asl.auto.arima model. Information for the model is provided as follows: forecast::auto.arima() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.climate.window.json b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.climate.window.json new file mode 100644 index 000000000..514ed0bab --- /dev/null +++ b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.climate.window.json @@ -0,0 +1,233 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "asl.climate.window_DO_mgL_mean_P1D_scores", + "bbox": [ + -79.8372, + 37.3032, + -79.8159, + 37.3129 + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [ + -79.8159, + 37.3129 + ], + [ + -79.8372, + 37.3032 + ] + ] + }, + "properties": { + "title": "asl.climate.window", + "description": "All scores for the Daily_oxygen_concentration variable for the asl.climate.window model. Information for the model is provided as follows: Climatology, but with a rolling 10-day window for historical data. This works really well for my methane forecasts at SERC, so I thought it might be useful here.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-06T00:00:00Z", + "start_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", + "providers": [ + { + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "http://ecoforecast.centers.vt.edu/", + "name": "Virginia Tech Center for Ecosystem Forecasting", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Scores", + "vera4cast", + "Chemical", + "asl.climate.window", + "oxygen_concentration", + "DO_mgL_mean", + "Daily", + "P1D", + "bvre", + "fcre", + "empirical" + ], + "table:columns": [ + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." + }, + { + "name": "datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: \u201censemble.\u201d Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The \u201csample\u201d distribution is synonymous with \u201censemble.\u201d For summary statistics: \u201csummary.\u201dIf this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "observation", + "type": "double", + "description": "observed value for variable" + }, + { + "name": "crps", + "type": "double", + "description": "crps forecast score" + }, + { + "name": "logs", + "type": "double", + "description": "logs forecast score" + }, + { + "name": "mean", + "type": "double", + "description": "mean forecast prediction" + }, + { + "name": "median", + "type": "double", + "description": "median forecast prediction" + }, + { + "name": "sd", + "type": "double", + "description": "standard deviation forecasts" + }, + { + "name": "quantile97.5", + "type": "double", + "description": "upper 97.5 percentile value of forecast" + }, + { + "name": "quantile02.5", + "type": "double", + "description": "upper 2.5 percentile value of forecast" + }, + { + "name": "quantile90", + "type": "double", + "description": "upper 90 percentile value of forecast" + }, + { + "name": "quantile10", + "type": "double", + "description": "upper 10 percentile value of forecast" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + }, + { + "name": "project_id", + "type": "string", + "description": "unique project identifier" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + } + ] + }, + "collection": "scores", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "asl.climate.window" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "asl.climate.window" + }, + { + "rel": "self", + "href": "asl.climate.window.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "type": "text/html", + "title": "Link for Model Code" + }, + { + "rel": "item", + "href": "https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/LTREB-reservoirs/vera4cast/main/catalog/scores/Chemical/Daily_oxygen_concentration/models/asl.climate.window.json", + "type": "text/html", + "title": "Link for rendered STAC item" + }, + { + "rel": "item", + "href": "https://raw.githubusercontent.com/LTREB-reservoirs/vera4cast/main/catalog/scores/Chemical/Daily_oxygen_concentration/models/asl.climate.window.json", + "type": "text/html", + "title": "Link for raw JSON file" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.climate.window.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.climate.window.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily oxygen_concentration", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/bundled-parquetproject_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.climate.window?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/bundled-parquetproject_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.climate.window?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} \ No newline at end of file diff --git a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.ets.json b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.ets.json index a0661e4ca..e4bca7f05 100644 --- a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.ets.json +++ b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.ets.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.ets", "description": "All scores for the Daily_oxygen_concentration variable for the asl.ets model. Information for the model is provided as follows: forecast::ets() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.met.lm.json b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.met.lm.json index a1db55f29..566b6a721 100644 --- a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.met.lm.json +++ b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.met.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm", "description": "All scores for the Daily_oxygen_concentration variable for the asl.met.lm model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.met.lm.step.json b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.met.lm.step.json index 5639967f9..f0e8e1179 100644 --- a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.met.lm.step.json +++ b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.met.lm.step.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm.step", "description": "All scores for the Daily_oxygen_concentration variable for the asl.met.lm.step model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-19T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.tbats.json b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.tbats.json index abb367b83..3cde9f1ed 100644 --- a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.tbats.json +++ b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.tbats.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.tbats", "description": "All scores for the Daily_oxygen_concentration variable for the asl.tbats model. Information for the model is provided as follows: forecast::tbats() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-20T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.temp.lm.json b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.temp.lm.json index d381803f9..7b78a0132 100644 --- a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.temp.lm.json +++ b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/asl.temp.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.temp.lm", "description": "All scores for the Daily_oxygen_concentration variable for the asl.temp.lm model. Information for the model is provided as follows: Linear regression with air temperature.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/climatology.json b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/climatology.json index 43796ef29..5db4ec7f9 100644 --- a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/climatology.json +++ b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/climatology.json @@ -27,9 +27,9 @@ "properties": { "title": "climatology", "description": "All scores for the Daily_oxygen_concentration variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/fableNNETAR.json b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/fableNNETAR.json index 1ca7e1103..999ba7f86 100644 --- a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/fableNNETAR.json +++ b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/fableNNETAR.json @@ -27,7 +27,7 @@ "properties": { "title": "fableNNETAR", "description": "All scores for the Daily_oxygen_concentration variable for the fableNNETAR model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", "end_datetime": "2024-06-03T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/fableNNETAR_focal.json b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/fableNNETAR_focal.json index 433cbad61..79ec68178 100644 --- a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/fableNNETAR_focal.json +++ b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/fableNNETAR_focal.json @@ -27,9 +27,9 @@ "properties": { "title": "fableNNETAR_focal", "description": "All scores for the Daily_oxygen_concentration variable for the fableNNETAR_focal model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/addelany/vera4casts/blob/main/code/combined_workflow/nnetar_workflow.R", diff --git a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/glm_aed_v1.json b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/glm_aed_v1.json index 1737d30c2..784a8cf28 100644 --- a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/glm_aed_v1.json +++ b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/glm_aed_v1.json @@ -23,9 +23,9 @@ "properties": { "title": "glm_aed_v1", "description": "All scores for the Daily_oxygen_concentration variable for the glm_aed_v1 model. Information for the model is provided as follows: GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters..\n The model predicts this variable at the following sites: fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-14T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/FLARE-forecast/FCRE-forecast-code/blob/main/workflows/glm_aed/combined_run_aed.R", diff --git a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/historic_mean.json b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/historic_mean.json index 3a38eb7fd..560b7ee85 100644 --- a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/historic_mean.json +++ b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/historic_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "historic_mean", "description": "All scores for the Daily_oxygen_concentration variable for the historic_mean model. Information for the model is provided as follows: Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model..\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/monthly_mean.json b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/monthly_mean.json index 5223172a2..e92cb0549 100644 --- a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/monthly_mean.json +++ b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/monthly_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "monthly_mean", "description": "All scores for the Daily_oxygen_concentration variable for the monthly_mean model. Information for the model is provided as follows: This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast..\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/persistenceRW.json b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/persistenceRW.json index 721ccc6fd..671b00978 100644 --- a/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/persistenceRW.json +++ b/data/challenge/vera4cast-stac/scores/Chemical/Daily_oxygen_concentration/models/persistenceRW.json @@ -27,9 +27,9 @@ "properties": { "title": "persistenceRW", "description": "All scores for the Daily_oxygen_concentration variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/scores/Chemical/collection.json b/data/challenge/vera4cast-stac/scores/Chemical/collection.json index 47b2fba3a..37162061f 100644 --- a/data/challenge/vera4cast-stac/scores/Chemical/collection.json +++ b/data/challenge/vera4cast-stac/scores/Chemical/collection.json @@ -73,7 +73,7 @@ "interval": [ [ "2023-09-21T00:00:00Z", - "2024-09-04T00:00:00Z" + "2024-09-05T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/collection.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/collection.json index cc2b2dbca..d95f3205b 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/collection.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/collection.json @@ -11,12 +11,12 @@ { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/asl.ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/secchi_last3obs_mean.json" + "href": "./models/asl.met.lm.step.json" }, { "rel": "item", @@ -26,7 +26,7 @@ { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.step.json" + "href": "./models/asl.met.lm.json" }, { "rel": "item", @@ -36,47 +36,47 @@ { "rel": "item", "type": "application/json", - "href": "./models/asl.temp.lm.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/fableNNETAR.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableNNETAR.json" + "href": "./models/fableNNETAR_focal.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableNNETAR_focal.json" + "href": "./models/historic_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/monthly_mean.json" + "href": "./models/glm_aed_v1.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.ets.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.json" + "href": "./models/secchi_last3obs_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/glm_aed_v1.json" + "href": "./models/asl.temp.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/historic_mean.json" + "href": "./models/monthly_mean.json" }, { "rel": "parent", diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.auto.arima.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.auto.arima.json index b3b1ca20f..caac406e7 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.auto.arima.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.auto.arima.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.auto.arima", "description": "All scores for the Daily_Secchi variable for the asl.auto.arima model. Information for the model is provided as follows: forecast::auto.arima() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", "end_datetime": "2024-08-26T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.ets.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.ets.json index 3100b6fd6..3fd66c9bc 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.ets.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.ets.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.ets", "description": "All scores for the Daily_Secchi variable for the asl.ets model. Information for the model is provided as follows: forecast::ets() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", "end_datetime": "2024-08-26T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.met.lm.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.met.lm.json index 92cbc2732..b6810e59e 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.met.lm.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.met.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm", "description": "All scores for the Daily_Secchi variable for the asl.met.lm model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.met.lm.step.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.met.lm.step.json index d331da8b4..19ae67959 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.met.lm.step.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.met.lm.step.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm.step", "description": "All scores for the Daily_Secchi variable for the asl.met.lm.step model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-19T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.tbats.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.tbats.json index c5b439695..640c8bc74 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.tbats.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.tbats.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.tbats", "description": "All scores for the Daily_Secchi variable for the asl.tbats model. Information for the model is provided as follows: forecast::tbats() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-20T00:00:00Z", "end_datetime": "2024-08-26T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.temp.lm.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.temp.lm.json index 15e3ac6ec..1be851649 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.temp.lm.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/asl.temp.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.temp.lm", "description": "All scores for the Daily_Secchi variable for the asl.temp.lm model. Information for the model is provided as follows: Linear regression with air temperature.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/climatology.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/climatology.json index 156e11e0f..39b4c130c 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/climatology.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/climatology.json @@ -14,20 +14,20 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -79.8159, - 37.3129 - ], [ -79.8372, 37.3032 + ], + [ + -79.8159, + 37.3129 ] ] }, "properties": { "title": "climatology", - "description": "All scores for the Daily_Secchi variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All scores for the Daily_Secchi variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: fcre, bvre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", "end_datetime": "2024-08-26T00:00:00Z", "providers": [ @@ -58,8 +58,8 @@ "Secchi_m_sample", "Daily", "P1D", - "bvre", "fcre", + "bvre", "empirical" ], "table:columns": [ diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/fableNNETAR.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/fableNNETAR.json index 8cc723b8a..f9c9c9c56 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/fableNNETAR.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/fableNNETAR.json @@ -27,7 +27,7 @@ "properties": { "title": "fableNNETAR", "description": "All scores for the Daily_Secchi variable for the fableNNETAR model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", "end_datetime": "2024-06-03T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/fableNNETAR_focal.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/fableNNETAR_focal.json index 38196b542..976c32ab9 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/fableNNETAR_focal.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/fableNNETAR_focal.json @@ -27,7 +27,7 @@ "properties": { "title": "fableNNETAR_focal", "description": "All scores for the Daily_Secchi variable for the fableNNETAR_focal model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", "end_datetime": "2024-08-26T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/glm_aed_v1.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/glm_aed_v1.json index bc72db42b..cf478690e 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/glm_aed_v1.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/glm_aed_v1.json @@ -23,7 +23,7 @@ "properties": { "title": "glm_aed_v1", "description": "All scores for the Daily_Secchi variable for the glm_aed_v1 model. Information for the model is provided as follows: GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters..\n The model predicts this variable at the following sites: fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-14T00:00:00Z", "end_datetime": "2024-08-26T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/historic_mean.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/historic_mean.json index 7114e172b..84b86f2e7 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/historic_mean.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/historic_mean.json @@ -27,7 +27,7 @@ "properties": { "title": "historic_mean", "description": "All scores for the Daily_Secchi variable for the historic_mean model. Information for the model is provided as follows: Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model..\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", "end_datetime": "2024-08-26T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/monthly_mean.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/monthly_mean.json index b711701ff..e38f4450a 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/monthly_mean.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/monthly_mean.json @@ -27,7 +27,7 @@ "properties": { "title": "monthly_mean", "description": "All scores for the Daily_Secchi variable for the monthly_mean model. Information for the model is provided as follows: This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast..\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", "end_datetime": "2024-08-26T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/persistenceRW.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/persistenceRW.json index 44d9e29b4..ab7e159c6 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/persistenceRW.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/persistenceRW.json @@ -27,7 +27,7 @@ "properties": { "title": "persistenceRW", "description": "All scores for the Daily_Secchi variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", "end_datetime": "2024-08-26T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/secchi_last3obs_mean.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/secchi_last3obs_mean.json index 2f3f9f767..f372f817b 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/secchi_last3obs_mean.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Secchi/models/secchi_last3obs_mean.json @@ -23,7 +23,7 @@ "properties": { "title": "secchi_last3obs_mean", "description": "All scores for the Daily_Secchi variable for the secchi_last3obs_mean model. Information for the model is provided as follows: This forecast simply takes the mean of the last three secchi observations and uses the standard deviation of that mean for the uncertainty around the forecast..\n The model predicts this variable at the following sites: fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-02T00:00:00Z", "end_datetime": "2024-08-26T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/collection.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/collection.json index da92a6772..157b4aba0 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/collection.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/collection.json @@ -21,7 +21,12 @@ { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.json" + "href": "./models/asl.met.lm.step.json" + }, + { + "rel": "item", + "type": "application/json", + "href": "./models/asl.tbats.json" }, { "rel": "item", @@ -31,67 +36,67 @@ { "rel": "item", "type": "application/json", - "href": "./models/asl.tbats.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableNNETAR_focal.json" + "href": "./models/fableNNETAR.json" }, { "rel": "item", "type": "application/json", - "href": "./models/historic_mean.json" + "href": "./models/fableNNETAR_focal.json" }, { "rel": "item", "type": "application/json", - "href": "./models/glm_aed_v1.json" + "href": "./models/flareGOTM.json" }, { "rel": "item", "type": "application/json", - "href": "./models/inflow_gefsClimAED.json" + "href": "./models/flareSimstrat.json" }, { "rel": "item", "type": "application/json", - "href": "./models/monthly_mean.json" + "href": "./models/historic_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/glm_aed_v1.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/monthly_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flareGOTM.json" + "href": "./models/persistenceFO.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flareSimstrat.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceFO.json" + "href": "./models/inflow_gefsClimAED.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.step.json" + "href": "./models/asl.met.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableNNETAR.json" + "href": "./models/asl.climate.window.json" }, { "rel": "parent", @@ -141,7 +146,7 @@ "interval": [ [ "2023-09-21T00:00:00Z", - "2024-09-03T00:00:00Z" + "2024-09-04T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.auto.arima.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.auto.arima.json index 6cd83eaa1..478bb6c82 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.auto.arima.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.auto.arima.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.auto.arima", "description": "All scores for the Daily_Water_temperature variable for the asl.auto.arima model. Information for the model is provided as follows: forecast::auto.arima() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.climate.window.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.climate.window.json new file mode 100644 index 000000000..bc3f5c281 --- /dev/null +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.climate.window.json @@ -0,0 +1,233 @@ +{ + "stac_version": "1.0.0", + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ], + "type": "Feature", + "id": "asl.climate.window_Temp_C_mean_P1D_scores", + "bbox": [ + -79.8372, + 37.3032, + -79.8159, + 37.3129 + ], + "geometry": { + "type": "MultiPoint", + "coordinates": [ + [ + -79.8159, + 37.3129 + ], + [ + -79.8372, + 37.3032 + ] + ] + }, + "properties": { + "title": "asl.climate.window", + "description": "All scores for the Daily_Water_temperature variable for the asl.climate.window model. Information for the model is provided as follows: Climatology, but with a rolling 10-day window for historical data. This works really well for my methane forecasts at SERC, so I thought it might be useful here.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-06T00:00:00Z", + "start_datetime": "2024-09-04T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", + "providers": [ + { + "url": "https://projects.ecoforecast.org/neon4cast-ci/", + "name": "NEON Ecological Forecasting Project", + "roles": [ + "producer", + "processor", + "licensor" + ] + }, + { + "url": "http://ecoforecast.centers.vt.edu/", + "name": "Virginia Tech Center for Ecosystem Forecasting", + "roles": [ + "host" + ] + } + ], + "license": "CC0-1.0", + "keywords": [ + "Scores", + "vera4cast", + "Physical", + "asl.climate.window", + "Water_temperature", + "Temp_C_mean", + "Daily", + "P1D", + "bvre", + "fcre", + "empirical" + ], + "table:columns": [ + { + "name": "reference_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "name": "site_id", + "type": "string", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." + }, + { + "name": "datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "name": "family", + "type": "string", + "description": "For ensembles: \u201censemble.\u201d Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The \u201csample\u201d distribution is synonymous with \u201censemble.\u201d For summary statistics: \u201csummary.\u201dIf this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." + }, + { + "name": "pub_datetime", + "type": "timestamp[us, tz=UTC]", + "description": "datetime that forecast was submitted" + }, + { + "name": "depth_m", + "type": "double", + "description": "depth (meters) in water column of prediction" + }, + { + "name": "observation", + "type": "double", + "description": "observed value for variable" + }, + { + "name": "crps", + "type": "double", + "description": "crps forecast score" + }, + { + "name": "logs", + "type": "double", + "description": "logs forecast score" + }, + { + "name": "mean", + "type": "double", + "description": "mean forecast prediction" + }, + { + "name": "median", + "type": "double", + "description": "median forecast prediction" + }, + { + "name": "sd", + "type": "double", + "description": "standard deviation forecasts" + }, + { + "name": "quantile97.5", + "type": "double", + "description": "upper 97.5 percentile value of forecast" + }, + { + "name": "quantile02.5", + "type": "double", + "description": "upper 2.5 percentile value of forecast" + }, + { + "name": "quantile90", + "type": "double", + "description": "upper 90 percentile value of forecast" + }, + { + "name": "quantile10", + "type": "double", + "description": "upper 10 percentile value of forecast" + }, + { + "name": "duration", + "type": "string", + "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" + }, + { + "name": "model_id", + "type": "string", + "description": "unique model identifier" + }, + { + "name": "project_id", + "type": "string", + "description": "unique project identifier" + }, + { + "name": "variable", + "type": "string", + "description": "name of forecasted variable" + } + ] + }, + "collection": "scores", + "links": [ + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "asl.climate.window" + }, + { + "rel": "root", + "href": "../../../catalog.json", + "type": "application/json", + "title": "Forecast Catalog" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "asl.climate.window" + }, + { + "rel": "self", + "href": "asl.climate.window.json", + "type": "application/json", + "title": "Model Forecast" + }, + { + "rel": "item", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "type": "text/html", + "title": "Link for Model Code" + }, + { + "rel": "item", + "href": "https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/LTREB-reservoirs/vera4cast/main/catalog/scores/Physical/Daily_Water_temperature/models/asl.climate.window.json", + "type": "text/html", + "title": "Link for rendered STAC item" + }, + { + "rel": "item", + "href": "https://raw.githubusercontent.com/LTREB-reservoirs/vera4cast/main/catalog/scores/Physical/Daily_Water_temperature/models/asl.climate.window.json", + "type": "text/html", + "title": "Link for raw JSON file" + } + ], + "assets": { + "1": { + "type": "application/json", + "title": "Model Metadata", + "href": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.climate.window.json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.climate.window.json\")\n\n" + }, + "2": { + "type": "text/html", + "title": "Link for Model Code", + "href": "https://github.com/abbylewis/vera_meteor_strike", + "description": "The link to the model code provided by the model submission team" + }, + "3": { + "type": "application/x-parquet", + "title": "Database Access for Daily Water_temperature", + "href": "s3://anonymous@bio230121-bucket01/vera4cast/scores/bundled-parquetproject_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.climate.window?endpoint_override=renc.osn.xsede.org", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/bundled-parquetproject_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.climate.window?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n" + } + } +} \ No newline at end of file diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.ets.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.ets.json index eecc23341..679e0c819 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.ets.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.ets.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.ets", "description": "All scores for the Daily_Water_temperature variable for the asl.ets model. Information for the model is provided as follows: forecast::ets() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.met.lm.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.met.lm.json index 97f79bb0c..78aad5607 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.met.lm.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.met.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm", "description": "All scores for the Daily_Water_temperature variable for the asl.met.lm model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.met.lm.step.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.met.lm.step.json index 8c5f9a16c..d71326c5f 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.met.lm.step.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.met.lm.step.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm.step", "description": "All scores for the Daily_Water_temperature variable for the asl.met.lm.step model. Information for the model is provided as follows: Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-19T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.tbats.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.tbats.json index 3eb90c15f..145543a83 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.tbats.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.tbats.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.tbats", "description": "All scores for the Daily_Water_temperature variable for the asl.tbats model. Information for the model is provided as follows: forecast::tbats() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-20T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.temp.lm.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.temp.lm.json index 2f3ced711..453d19db3 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.temp.lm.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/asl.temp.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.temp.lm", "description": "All scores for the Daily_Water_temperature variable for the asl.temp.lm model. Information for the model is provided as follows: Linear regression with air temperature.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/climatology.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/climatology.json index 636437981..e0a128380 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/climatology.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/climatology.json @@ -14,10 +14,6 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -79.8357, - 37.3078 - ], [ -79.8159, 37.3129 @@ -25,15 +21,19 @@ [ -79.8372, 37.3032 + ], + [ + -79.8357, + 37.3078 ] ] }, "properties": { "title": "climatology", - "description": "All scores for the Daily_Water_temperature variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: tubr, bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "description": "All scores for the Daily_Water_temperature variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: bvre, fcre, tubr.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-09-21T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", @@ -62,9 +62,9 @@ "Temp_C_mean", "Daily", "P1D", - "tubr", "bvre", "fcre", + "tubr", "empirical" ], "table:columns": [ diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/fableNNETAR.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/fableNNETAR.json index e57db07e1..e435b4639 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/fableNNETAR.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/fableNNETAR.json @@ -27,7 +27,7 @@ "properties": { "title": "fableNNETAR", "description": "All scores for the Daily_Water_temperature variable for the fableNNETAR model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", "end_datetime": "2024-06-03T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/fableNNETAR_focal.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/fableNNETAR_focal.json index ce9091bc3..016aa0160 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/fableNNETAR_focal.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/fableNNETAR_focal.json @@ -27,9 +27,9 @@ "properties": { "title": "fableNNETAR_focal", "description": "All scores for the Daily_Water_temperature variable for the fableNNETAR_focal model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/addelany/vera4casts/blob/main/code/combined_workflow/nnetar_workflow.R", diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/flareGOTM.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/flareGOTM.json index a7c09a2a6..077986f5e 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/flareGOTM.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/flareGOTM.json @@ -23,9 +23,9 @@ "properties": { "title": "flareGOTM", "description": "All scores for the Daily_Water_temperature variable for the flareGOTM model. Information for the model is provided as follows: FLARE-GOTM combines the 1D hydrodynamic process-based model GOTM, a data assimilation algorithm, and NOAA weather data to forecast water column temperatures..\n The model predicts this variable at the following sites: fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-01-21T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": null, diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/flareSimstrat.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/flareSimstrat.json index 78fee2146..015db6667 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/flareSimstrat.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/flareSimstrat.json @@ -23,9 +23,9 @@ "properties": { "title": "flareSimstrat", "description": "All scores for the Daily_Water_temperature variable for the flareSimstrat model. Information for the model is provided as follows: FLARE-Simstrat combines the 1D process-based model Simstrat, a data assimilation algorithm (EnKF) and NOAA driver weather data to make predictions of water column temperatures..\n The model predicts this variable at the following sites: fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-01-21T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/FLARE-forecast/FCRE-forecast-code/blob/main/workflows/ler/combined_workflow_Simstrat.R", diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/glm_aed_v1.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/glm_aed_v1.json index 455c54c87..0d26bfe26 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/glm_aed_v1.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/glm_aed_v1.json @@ -23,9 +23,9 @@ "properties": { "title": "glm_aed_v1", "description": "All scores for the Daily_Water_temperature variable for the glm_aed_v1 model. Information for the model is provided as follows: GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters..\n The model predicts this variable at the following sites: fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-14T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/FLARE-forecast/FCRE-forecast-code/blob/main/workflows/glm_aed/combined_run_aed.R", diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/historic_mean.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/historic_mean.json index d9f13759e..0aafd66ba 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/historic_mean.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/historic_mean.json @@ -31,9 +31,9 @@ "properties": { "title": "historic_mean", "description": "All scores for the Daily_Water_temperature variable for the historic_mean model. Information for the model is provided as follows: Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model..\n The model predicts this variable at the following sites: bvre, fcre, tubr.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/inflow_gefsClimAED.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/inflow_gefsClimAED.json index 1809d42f2..a2d0e8cff 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/inflow_gefsClimAED.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/inflow_gefsClimAED.json @@ -23,9 +23,9 @@ "properties": { "title": "inflow_gefsClimAED", "description": "All scores for the Daily_Water_temperature variable for the inflow_gefsClimAED model. Information for the model is provided as follows: flow is forecasted as using a linear relationship between historical flow, month, and 5-day sum of precipitation. Temperature is forecasted using a linear relationship between historical water temperature, month, and 5-day mean air temperature. NOAA GEFS is then used to get the future values of 5-day sum precipitation and mean temperature. Nutrients are forecasting using the DOY climatology. The DOY climatology was developed using a linear interpolation of the historical samples..\n The model predicts this variable at the following sites: tubr.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-13T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast_models/blob/main/inflow_aed.R", diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/monthly_mean.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/monthly_mean.json index 95edada36..a0d571360 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/monthly_mean.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/monthly_mean.json @@ -31,9 +31,9 @@ "properties": { "title": "monthly_mean", "description": "All scores for the Daily_Water_temperature variable for the monthly_mean model. Information for the model is provided as follows: This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast..\n The model predicts this variable at the following sites: bvre, fcre, tubr.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/persistenceFO.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/persistenceFO.json index a23f1fe53..b8436cd03 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/persistenceFO.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/persistenceFO.json @@ -23,7 +23,7 @@ "properties": { "title": "persistenceFO", "description": "All scores for the Daily_Water_temperature variable for the persistenceFO model. Information for the model is provided as follows: another persistence forecast.\n The model predicts this variable at the following sites: bvre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-09-27T00:00:00Z", "end_datetime": "2023-10-30T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/persistenceRW.json b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/persistenceRW.json index 8bb4c5959..6be6e93d5 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/persistenceRW.json +++ b/data/challenge/vera4cast-stac/scores/Physical/Daily_Water_temperature/models/persistenceRW.json @@ -31,9 +31,9 @@ "properties": { "title": "persistenceRW", "description": "All scores for the Daily_Water_temperature variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: bvre, fcre, tubr.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-09-21T00:00:00Z", - "end_datetime": "2024-09-03T00:00:00Z", + "end_datetime": "2024-09-04T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/scores/Physical/collection.json b/data/challenge/vera4cast-stac/scores/Physical/collection.json index 116613883..258285e35 100644 --- a/data/challenge/vera4cast-stac/scores/Physical/collection.json +++ b/data/challenge/vera4cast-stac/scores/Physical/collection.json @@ -76,7 +76,7 @@ "interval": [ [ "2023-09-21T00:00:00Z", - "2024-09-04T00:00:00Z" + "2024-09-05T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/scores/collection.json b/data/challenge/vera4cast-stac/scores/collection.json index fe53860e0..94b11a791 100644 --- a/data/challenge/vera4cast-stac/scores/collection.json +++ b/data/challenge/vera4cast-stac/scores/collection.json @@ -74,7 +74,7 @@ "interval": [ [ "2023-09-21T00:00:00Z", - "2024-09-04T00:00:00Z" + "2024-09-05T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/sites/collection.json b/data/challenge/vera4cast-stac/sites/collection.json index d71d2f03e..26a026f50 100644 --- a/data/challenge/vera4cast-stac/sites/collection.json +++ b/data/challenge/vera4cast-stac/sites/collection.json @@ -58,7 +58,7 @@ "interval": [ [ "2013-03-07T00:00:00Z", - "2024-09-04T00:00:00Z" + "2024-09-05T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/collection.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/collection.json index e6635139a..754eee3a6 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/collection.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/collection.json @@ -16,77 +16,77 @@ { "rel": "item", "type": "application/json", - "href": "./models/asl.ets.json" + "href": "./models/asl.climate.window.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.json" + "href": "./models/asl.ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.tbats.json" + "href": "./models/asl.met.lm.step.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.temp.lm.json" + "href": "./models/asl.met.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/asl.persistence.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableARIMA.json" + "href": "./models/asl.tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableETS.json" + "href": "./models/asl.temp.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableNNETAR.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/glm_aed_v1.json" + "href": "./models/fableARIMA.json" }, { "rel": "item", "type": "application/json", - "href": "./models/historic_mean.json" + "href": "./models/fableETS.json" }, { "rel": "item", "type": "application/json", - "href": "./models/monthly_mean.json" + "href": "./models/fableNNETAR.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/glm_aed_v1.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.climate.window.json" + "href": "./models/historic_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.step.json" + "href": "./models/monthly_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.persistence.json" + "href": "./models/persistenceRW.json" }, { "rel": "parent", @@ -136,7 +136,7 @@ "interval": [ [ "2023-10-01T00:00:00Z", - "2024-10-10T00:00:00Z" + "2024-10-11T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.auto.arima.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.auto.arima.json index 23fbcd762..a444fd46a 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.auto.arima.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.auto.arima.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.auto.arima", "description": "forecast::auto.arima() function in R, fit individually at each site/depth", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-04-29T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.climate.window.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.climate.window.json index e3f583328..a5be1761e 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.climate.window.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.climate.window.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.climate.window", "description": "Climatology, but with a rolling 10-day window for historical data. This works really well for my methane forecasts at SERC, so I thought it might be useful here", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-09-04T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.ets.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.ets.json index a7a5f3820..305094051 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.ets.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.ets.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.ets", "description": "forecast::ets() function in R, fit individually at each site/depth", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-04-29T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.met.lm.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.met.lm.json index faa3f9da6..7a8d4639f 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.met.lm.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.met.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm", "description": "Linear regression with air temp, humidity, precip, and wind speed", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-14T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.met.lm.step.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.met.lm.step.json index c815a6d36..015409c6e 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.met.lm.step.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.met.lm.step.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm.step", "description": "Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-14T00:00:00Z", "end_datetime": "2024-06-19T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.persistence.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.persistence.json index ff102022f..dab8d7841 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.persistence.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.persistence.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.persistence", "description": "The random walk persistence model has massive uncertainty at long horizons, but you could instead re-fit the persistence model for every forecast horizon to get a more precise forecast (i.e., not dynamic). I did this for my work at SERC, and am submitting here in case it is helpful", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-09-05T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.tbats.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.tbats.json index 4ff16e5b0..aa0f3d133 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.tbats.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.tbats.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.tbats", "description": "forecast::tbats() function in R, fit individually at each site/depth", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-04-29T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.temp.lm.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.temp.lm.json index 014f8b93f..cf0e09665 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.temp.lm.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/asl.temp.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.temp.lm", "description": "Linear regression with air temperature", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-14T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/climatology.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/climatology.json index 60033f796..62f6c5059 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/climatology.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/climatology.json @@ -27,9 +27,9 @@ "properties": { "title": "climatology", "description": "Historical DOY mean and sd. Assumes normal distribution", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-09T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/fableARIMA.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/fableARIMA.json index 9c1eda93f..dd404e28e 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/fableARIMA.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/fableARIMA.json @@ -27,9 +27,9 @@ "properties": { "title": "fableARIMA", "description": "ARIMA fit using the ARIMA() function in the fable R package", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableARIMA.R", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/fableETS.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/fableETS.json index 3efa0f2ed..a0c004e3b 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/fableETS.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/fableETS.json @@ -27,9 +27,9 @@ "properties": { "title": "fableETS", "description": "fable package exponential smoothing model fable::ETS()", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableETS.R", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/fableNNETAR.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/fableNNETAR.json index 8ba5454de..8edc57257 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/fableNNETAR.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/fableNNETAR.json @@ -27,9 +27,9 @@ "properties": { "title": "fableNNETAR", "description": "autoregressive neural net fit using the NNETAR() function in the fable R package", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableNNETAR.R", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/glm_aed_v1.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/glm_aed_v1.json index 5387e0345..4154e25a1 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/glm_aed_v1.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/glm_aed_v1.json @@ -23,7 +23,7 @@ "properties": { "title": "glm_aed_v1", "description": "GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters.", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-20T00:00:00Z", "end_datetime": "2024-10-05T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/historic_mean.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/historic_mean.json index 87f6440a1..69fbc9215 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/historic_mean.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/historic_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "historic_mean", "description": "Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model.", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-09T00:00:00Z", - "end_datetime": "2024-10-08T00:00:00Z", + "end_datetime": "2024-10-09T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/monthly_mean.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/monthly_mean.json index 31be2b53c..5c4bdf462 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/monthly_mean.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/monthly_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "monthly_mean", "description": "This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast.", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-09T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/persistenceRW.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/persistenceRW.json index dc7ea701b..a0c8cd1ec 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/persistenceRW.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Bloom_binary/models/persistenceRW.json @@ -27,9 +27,9 @@ "properties": { "title": "persistenceRW", "description": "Random walk from the fable package with ensembles used to represent uncertainty", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-09T00:00:00Z", - "end_datetime": "2024-10-08T00:00:00Z", + "end_datetime": "2024-10-09T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/collection.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/collection.json index 8f42d0c26..7773d8636 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/collection.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/collection.json @@ -11,77 +11,77 @@ { "rel": "item", "type": "application/json", - "href": "./models/asl.auto.arima.json" + "href": "./models/asl.tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.ets.json" + "href": "./models/asl.temp.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.step.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.json" + "href": "./models/fableARIMA.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.tbats.json" + "href": "./models/fableETS.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.temp.lm.json" + "href": "./models/fableNNETAR.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/glm_aed_v1.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableARIMA.json" + "href": "./models/asl.auto.arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableETS.json" + "href": "./models/asl.climate.window.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableNNETAR.json" + "href": "./models/asl.ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/glm_aed_v1.json" + "href": "./models/asl.met.lm.step.json" }, { "rel": "item", "type": "application/json", - "href": "./models/historic_mean.json" + "href": "./models/asl.met.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/monthly_mean.json" + "href": "./models/historic_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/monthly_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.climate.window.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", @@ -136,7 +136,7 @@ "interval": [ [ "2023-10-01T00:00:00Z", - "2024-10-10T00:00:00Z" + "2024-10-11T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.auto.arima.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.auto.arima.json index 866f579e9..9533b271b 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.auto.arima.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.auto.arima.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.auto.arima", "description": "forecast::auto.arima() function in R, fit individually at each site/depth", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.climate.window.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.climate.window.json index 65891df56..1ab9041cc 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.climate.window.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.climate.window.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.climate.window", "description": "Climatology, but with a rolling 10-day window for historical data. This works really well for my methane forecasts at SERC, so I thought it might be useful here", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-09-04T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.ets.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.ets.json index afc69e4ff..172f6bf68 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.ets.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.ets.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.ets", "description": "forecast::ets() function in R, fit individually at each site/depth", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.met.lm.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.met.lm.json index 18cd12314..d57b281e5 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.met.lm.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.met.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm", "description": "Linear regression with air temp, humidity, precip, and wind speed", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.met.lm.step.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.met.lm.step.json index 3bfacbbaf..4b513b1bb 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.met.lm.step.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.met.lm.step.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm.step", "description": "Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-19T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.persistence.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.persistence.json index 156b0e6ce..64388f192 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.persistence.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.persistence.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.persistence", "description": "The random walk persistence model has massive uncertainty at long horizons, but you could instead re-fit the persistence model for every forecast horizon to get a more precise forecast (i.e., not dynamic). I did this for my work at SERC, and am submitting here in case it is helpful", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-09-05T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.tbats.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.tbats.json index 5e836938f..5a9a58b7a 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.tbats.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.tbats.json @@ -14,22 +14,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -79.8159, - 37.3129 - ], [ -79.8372, 37.3032 + ], + [ + -79.8159, + 37.3129 ] ] }, "properties": { "title": "asl.tbats", "description": "forecast::tbats() function in R, fit individually at each site/depth", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-20T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", @@ -58,8 +58,8 @@ "Chla_ugL_mean", "Daily", "P1D", - "bvre", "fcre", + "bvre", "empirical" ], "table:columns": [ diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.temp.lm.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.temp.lm.json index ea214b5cb..74645f27b 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.temp.lm.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/asl.temp.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.temp.lm", "description": "Linear regression with air temperature", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/climatology.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/climatology.json index 887dd1efc..ac703839a 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/climatology.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/climatology.json @@ -27,9 +27,9 @@ "properties": { "title": "climatology", "description": "Historical DOY mean and sd. Assumes normal distribution", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-02T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/fableARIMA.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/fableARIMA.json index 28b32429e..56c333b7f 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/fableARIMA.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/fableARIMA.json @@ -27,9 +27,9 @@ "properties": { "title": "fableARIMA", "description": "ARIMA fit using the ARIMA() function in the fable R package", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableARIMA.R", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/fableETS.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/fableETS.json index a916cdcd1..4646263d7 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/fableETS.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/fableETS.json @@ -27,9 +27,9 @@ "properties": { "title": "fableETS", "description": "fable package exponential smoothing model fable::ETS()", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableETS.R", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/fableNNETAR.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/fableNNETAR.json index 2146ec638..a120541e2 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/fableNNETAR.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/fableNNETAR.json @@ -14,22 +14,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -79.8372, - 37.3032 - ], [ -79.8159, 37.3129 + ], + [ + -79.8372, + 37.3032 ] ] }, "properties": { "title": "fableNNETAR", "description": "autoregressive neural net fit using the NNETAR() function in the fable R package", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/melofton/vera4casts/blob/main/code/function_library/predict/fableNNETAR.R", @@ -58,8 +58,8 @@ "Chla_ugL_mean", "Daily", "P1D", - "fcre", "bvre", + "fcre", "machine learning" ], "table:columns": [ diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/glm_aed_v1.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/glm_aed_v1.json index d32888158..31d0f9ded 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/glm_aed_v1.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/glm_aed_v1.json @@ -23,7 +23,7 @@ "properties": { "title": "glm_aed_v1", "description": "GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters.", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-14T00:00:00Z", "end_datetime": "2024-10-05T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/historic_mean.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/historic_mean.json index c0e44f613..0db3a092a 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/historic_mean.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/historic_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "historic_mean", "description": "Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model.", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-08T00:00:00Z", + "end_datetime": "2024-10-09T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/monthly_mean.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/monthly_mean.json index 7304bbc9f..d530d229e 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/monthly_mean.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/monthly_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "monthly_mean", "description": "This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast.", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/persistenceRW.json b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/persistenceRW.json index f9c6b48f2..073cd4cba 100644 --- a/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/persistenceRW.json +++ b/data/challenge/vera4cast-stac/summaries/Biological/Daily_Chlorophyll-a/models/persistenceRW.json @@ -27,9 +27,9 @@ "properties": { "title": "persistenceRW", "description": "Random walk from the fable package with ensembles used to represent uncertainty", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-01T00:00:00Z", - "end_datetime": "2024-10-08T00:00:00Z", + "end_datetime": "2024-10-09T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/collection.json b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/collection.json index 43a0a97ba..0afb0ebc2 100644 --- a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/collection.json +++ b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/collection.json @@ -13,6 +13,11 @@ "type": "application/json", "href": "./models/asl.auto.arima.json" }, + { + "rel": "item", + "type": "application/json", + "href": "./models/asl.climate.window.json" + }, { "rel": "item", "type": "application/json", @@ -73,11 +78,6 @@ "type": "application/json", "href": "./models/persistenceRW.json" }, - { - "rel": "item", - "type": "application/json", - "href": "./models/asl.climate.window.json" - }, { "rel": "item", "type": "application/json", @@ -131,7 +131,7 @@ "interval": [ [ "2023-10-14T00:00:00Z", - "2024-10-10T00:00:00Z" + "2024-10-11T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.auto.arima.json b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.auto.arima.json index d8f3ff18a..dd39f7015 100644 --- a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.auto.arima.json +++ b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.auto.arima.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.auto.arima", "description": "forecast::auto.arima() function in R, fit individually at each site/depth", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.climate.window.json b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.climate.window.json index 386339929..c3a7a3408 100644 --- a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.climate.window.json +++ b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.climate.window.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.climate.window", "description": "Climatology, but with a rolling 10-day window for historical data. This works really well for my methane forecasts at SERC, so I thought it might be useful here", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-09-04T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.ets.json b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.ets.json index de75de9aa..b17c70033 100644 --- a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.ets.json +++ b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.ets.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.ets", "description": "forecast::ets() function in R, fit individually at each site/depth", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.met.lm.json b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.met.lm.json index af9689b5b..3f30a29c5 100644 --- a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.met.lm.json +++ b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.met.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm", "description": "Linear regression with air temp, humidity, precip, and wind speed", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.met.lm.step.json b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.met.lm.step.json index 0ad213ceb..41a3a7728 100644 --- a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.met.lm.step.json +++ b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.met.lm.step.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm.step", "description": "Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-19T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.persistence.json b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.persistence.json index e7023751f..971275e34 100644 --- a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.persistence.json +++ b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.persistence.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.persistence", "description": "The random walk persistence model has massive uncertainty at long horizons, but you could instead re-fit the persistence model for every forecast horizon to get a more precise forecast (i.e., not dynamic). I did this for my work at SERC, and am submitting here in case it is helpful", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-09-05T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.tbats.json b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.tbats.json index 6a039457b..9489d459f 100644 --- a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.tbats.json +++ b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.tbats.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.tbats", "description": "forecast::tbats() function in R, fit individually at each site/depth", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-20T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.temp.lm.json b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.temp.lm.json index c7c507119..849014c97 100644 --- a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.temp.lm.json +++ b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/asl.temp.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.temp.lm", "description": "Linear regression with air temperature", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/climatology.json b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/climatology.json index a0b5a9ca2..e8a638c81 100644 --- a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/climatology.json +++ b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/climatology.json @@ -27,9 +27,9 @@ "properties": { "title": "climatology", "description": "Historical DOY mean and sd. Assumes normal distribution", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/fableNNETAR.json b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/fableNNETAR.json index 597f81e22..77512dbd2 100644 --- a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/fableNNETAR.json +++ b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/fableNNETAR.json @@ -14,20 +14,20 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -79.8159, - 37.3129 - ], [ -79.8372, 37.3032 + ], + [ + -79.8159, + 37.3129 ] ] }, "properties": { "title": "fableNNETAR", "description": "autoregressive neural net fit using the NNETAR() function in the fable R package", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", "end_datetime": "2024-06-03T00:00:00Z", "providers": [ @@ -58,8 +58,8 @@ "DO_mgL_mean", "Daily", "P1D", - "bvre", "fcre", + "bvre", "machine learning" ], "table:columns": [ diff --git a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/fableNNETAR_focal.json b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/fableNNETAR_focal.json index a6bc02341..9cb3a2cd7 100644 --- a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/fableNNETAR_focal.json +++ b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/fableNNETAR_focal.json @@ -14,22 +14,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -79.8372, - 37.3032 - ], [ -79.8159, 37.3129 + ], + [ + -79.8372, + 37.3032 ] ] }, "properties": { "title": "fableNNETAR_focal", "description": "autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/addelany/vera4casts/blob/main/code/combined_workflow/nnetar_workflow.R", @@ -58,8 +58,8 @@ "DO_mgL_mean", "Daily", "P1D", - "fcre", "bvre", + "fcre", "machine learning" ], "table:columns": [ diff --git a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/glm_aed_v1.json b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/glm_aed_v1.json index f378d649c..3449ea0cf 100644 --- a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/glm_aed_v1.json +++ b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/glm_aed_v1.json @@ -23,7 +23,7 @@ "properties": { "title": "glm_aed_v1", "description": "GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters.", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-14T00:00:00Z", "end_datetime": "2024-10-05T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/historic_mean.json b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/historic_mean.json index 325cff563..555f873be 100644 --- a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/historic_mean.json +++ b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/historic_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "historic_mean", "description": "Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model.", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-08T00:00:00Z", + "end_datetime": "2024-10-09T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/monthly_mean.json b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/monthly_mean.json index 7d1314b85..61eb0c028 100644 --- a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/monthly_mean.json +++ b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/monthly_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "monthly_mean", "description": "This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast.", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/persistenceRW.json b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/persistenceRW.json index 973147ab2..2c38a0056 100644 --- a/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/persistenceRW.json +++ b/data/challenge/vera4cast-stac/summaries/Chemical/Daily_oxygen_concentration/models/persistenceRW.json @@ -27,9 +27,9 @@ "properties": { "title": "persistenceRW", "description": "Random walk from the fable package with ensembles used to represent uncertainty", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-08T00:00:00Z", + "end_datetime": "2024-10-09T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/collection.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/collection.json index 4dfe34882..181eac598 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/collection.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/collection.json @@ -81,12 +81,12 @@ { "rel": "item", "type": "application/json", - "href": "./models/asl.climate.window.json" + "href": "./models/asl.persistence.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.persistence.json" + "href": "./models/asl.climate.window.json" }, { "rel": "parent", @@ -136,7 +136,7 @@ "interval": [ [ "2023-10-14T00:00:00Z", - "2024-10-10T00:00:00Z" + "2024-10-11T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.auto.arima.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.auto.arima.json index 9df1027ff..dac3f44bf 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.auto.arima.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.auto.arima.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.auto.arima", "description": "forecast::auto.arima() function in R, fit individually at each site/depth", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.climate.window.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.climate.window.json index 6f7a5f836..0d1ec39d5 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.climate.window.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.climate.window.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.climate.window", "description": "Climatology, but with a rolling 10-day window for historical data. This works really well for my methane forecasts at SERC, so I thought it might be useful here", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-09-04T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.ets.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.ets.json index e66fcf677..e959d9633 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.ets.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.ets.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.ets", "description": "forecast::ets() function in R, fit individually at each site/depth", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.met.lm.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.met.lm.json index 7a9c9a891..ebbc33814 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.met.lm.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.met.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm", "description": "Linear regression with air temp, humidity, precip, and wind speed", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.met.lm.step.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.met.lm.step.json index b17433334..3212dd2f0 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.met.lm.step.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.met.lm.step.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm.step", "description": "Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-19T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.persistence.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.persistence.json index 7e9f4a8a0..763d31d61 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.persistence.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.persistence.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.persistence", "description": "The random walk persistence model has massive uncertainty at long horizons, but you could instead re-fit the persistence model for every forecast horizon to get a more precise forecast (i.e., not dynamic). I did this for my work at SERC, and am submitting here in case it is helpful", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-09-05T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.tbats.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.tbats.json index 326700075..5a7452837 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.tbats.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.tbats.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.tbats", "description": "forecast::tbats() function in R, fit individually at each site/depth", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-20T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.temp.lm.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.temp.lm.json index a2c319ca6..eb3bbd600 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.temp.lm.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/asl.temp.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.temp.lm", "description": "Linear regression with air temperature", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/climatology.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/climatology.json index 0295180ea..e599f13b0 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/climatology.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/climatology.json @@ -27,9 +27,9 @@ "properties": { "title": "climatology", "description": "Historical DOY mean and sd. Assumes normal distribution", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/fableNNETAR.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/fableNNETAR.json index bdb20ca0a..8c6897f68 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/fableNNETAR.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/fableNNETAR.json @@ -27,7 +27,7 @@ "properties": { "title": "fableNNETAR", "description": "autoregressive neural net fit using the NNETAR() function in the fable R package", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", "end_datetime": "2024-06-03T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/fableNNETAR_focal.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/fableNNETAR_focal.json index 74ffe27d1..27ca63428 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/fableNNETAR_focal.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/fableNNETAR_focal.json @@ -14,22 +14,22 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -79.8372, - 37.3032 - ], [ -79.8159, 37.3129 + ], + [ + -79.8372, + 37.3032 ] ] }, "properties": { "title": "fableNNETAR_focal", "description": "autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/addelany/vera4casts/blob/main/code/combined_workflow/nnetar_workflow.R", @@ -58,8 +58,8 @@ "Secchi_m_sample", "Daily", "P1D", - "fcre", "bvre", + "fcre", "machine learning" ], "table:columns": [ diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/glm_aed_v1.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/glm_aed_v1.json index 91413a77e..de8be43e3 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/glm_aed_v1.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/glm_aed_v1.json @@ -23,7 +23,7 @@ "properties": { "title": "glm_aed_v1", "description": "GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters.", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-14T00:00:00Z", "end_datetime": "2024-10-05T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/historic_mean.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/historic_mean.json index 2e6147696..113128178 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/historic_mean.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/historic_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "historic_mean", "description": "Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model.", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-08T00:00:00Z", + "end_datetime": "2024-10-09T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/monthly_mean.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/monthly_mean.json index 4f1b05d52..e2b406528 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/monthly_mean.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/monthly_mean.json @@ -27,9 +27,9 @@ "properties": { "title": "monthly_mean", "description": "This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast.", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/persistenceRW.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/persistenceRW.json index 3f48cdccc..ff7e36bea 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/persistenceRW.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/persistenceRW.json @@ -27,9 +27,9 @@ "properties": { "title": "persistenceRW", "description": "Random walk from the fable package with ensembles used to represent uncertainty", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-08T00:00:00Z", + "end_datetime": "2024-10-09T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/secchi_last3obs_mean.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/secchi_last3obs_mean.json index e1a52aba5..01c88374d 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/secchi_last3obs_mean.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Secchi/models/secchi_last3obs_mean.json @@ -23,7 +23,7 @@ "properties": { "title": "secchi_last3obs_mean", "description": "This forecast simply takes the mean of the last three secchi observations and uses the standard deviation of that mean for the uncertainty around the forecast.", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-02T00:00:00Z", "end_datetime": "2024-09-20T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/collection.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/collection.json index f02967d99..d625f93cd 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/collection.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/collection.json @@ -11,97 +11,97 @@ { "rel": "item", "type": "application/json", - "href": "./models/asl.auto.arima.json" + "href": "./models/climatology.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.climate.window.json" + "href": "./models/fableNNETAR.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.ets.json" + "href": "./models/fableNNETAR_focal.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.step.json" + "href": "./models/flareGOTM.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.met.lm.json" + "href": "./models/flareSimstrat.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.persistence.json" + "href": "./models/glm_aed_v1.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.tbats.json" + "href": "./models/historic_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/asl.temp.lm.json" + "href": "./models/inflow_gefsClimAED.json" }, { "rel": "item", "type": "application/json", - "href": "./models/climatology.json" + "href": "./models/monthly_mean.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableNNETAR.json" + "href": "./models/persistenceRW.json" }, { "rel": "item", "type": "application/json", - "href": "./models/fableNNETAR_focal.json" + "href": "./models/asl.auto.arima.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flareGOTM.json" + "href": "./models/asl.ets.json" }, { "rel": "item", "type": "application/json", - "href": "./models/flareSimstrat.json" + "href": "./models/asl.met.lm.step.json" }, { "rel": "item", "type": "application/json", - "href": "./models/glm_aed_v1.json" + "href": "./models/asl.met.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/historic_mean.json" + "href": "./models/asl.persistence.json" }, { "rel": "item", "type": "application/json", - "href": "./models/inflow_gefsClimAED.json" + "href": "./models/asl.tbats.json" }, { "rel": "item", "type": "application/json", - "href": "./models/monthly_mean.json" + "href": "./models/asl.temp.lm.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceFO.json" + "href": "./models/asl.climate.window.json" }, { "rel": "item", "type": "application/json", - "href": "./models/persistenceRW.json" + "href": "./models/persistenceFO.json" }, { "rel": "parent", @@ -151,7 +151,7 @@ "interval": [ [ "2023-09-21T00:00:00Z", - "2024-10-10T00:00:00Z" + "2024-10-11T00:00:00Z" ] ] } diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.auto.arima.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.auto.arima.json index 2f51b9bd2..580ec8cd8 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.auto.arima.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.auto.arima.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.auto.arima", "description": "forecast::auto.arima() function in R, fit individually at each site/depth", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.climate.window.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.climate.window.json index 78baa72a9..545802821 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.climate.window.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.climate.window.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.climate.window", "description": "Climatology, but with a rolling 10-day window for historical data. This works really well for my methane forecasts at SERC, so I thought it might be useful here", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-09-04T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.ets.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.ets.json index ad64a1684..2429b04f7 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.ets.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.ets.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.ets", "description": "forecast::ets() function in R, fit individually at each site/depth", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-12T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.met.lm.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.met.lm.json index c7e28150a..13a470469 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.met.lm.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.met.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm", "description": "Linear regression with air temp, humidity, precip, and wind speed", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.met.lm.step.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.met.lm.step.json index b1947a6b0..d491e4d78 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.met.lm.step.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.met.lm.step.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.met.lm.step", "description": "Linear regression with air temp, humidity, precip, and wind speed. Model selected using AIC", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-19T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.persistence.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.persistence.json index 689b3f9d8..1f76b417f 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.persistence.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.persistence.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.persistence", "description": "The random walk persistence model has massive uncertainty at long horizons, but you could instead re-fit the persistence model for every forecast horizon to get a more precise forecast (i.e., not dynamic). I did this for my work at SERC, and am submitting here in case it is helpful", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-09-05T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://projects.ecoforecast.org/neon4cast-ci/", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.tbats.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.tbats.json index 7eeea1089..d82d2fdd7 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.tbats.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.tbats.json @@ -27,9 +27,9 @@ "properties": { "title": "asl.tbats", "description": "forecast::tbats() function in R, fit individually at each site/depth", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-03-20T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/abbylewis/vera_meteor_strike", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.temp.lm.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.temp.lm.json index 12692be28..caa7dec77 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.temp.lm.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/asl.temp.lm.json @@ -27,7 +27,7 @@ "properties": { "title": "asl.temp.lm", "description": "Linear regression with air temperature", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-05-06T00:00:00Z", "end_datetime": "2024-06-27T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/climatology.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/climatology.json index 0c95d480a..79f1087af 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/climatology.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/climatology.json @@ -14,6 +14,10 @@ "geometry": { "type": "MultiPoint", "coordinates": [ + [ + -79.8357, + 37.3078 + ], [ -79.8159, 37.3129 @@ -21,19 +25,15 @@ [ -79.8372, 37.3032 - ], - [ - -79.8357, - 37.3078 ] ] }, "properties": { "title": "climatology", "description": "Historical DOY mean and sd. Assumes normal distribution", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-09-21T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", @@ -62,9 +62,9 @@ "Temp_C_mean", "Daily", "P1D", + "tubr", "bvre", "fcre", - "tubr", "empirical" ], "table:columns": [ diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/fableNNETAR.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/fableNNETAR.json index c46d624b9..354c128cd 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/fableNNETAR.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/fableNNETAR.json @@ -14,20 +14,20 @@ "geometry": { "type": "MultiPoint", "coordinates": [ - [ - -79.8372, - 37.3032 - ], [ -79.8159, 37.3129 + ], + [ + -79.8372, + 37.3032 ] ] }, "properties": { "title": "fableNNETAR", "description": "autoregressive neural net fit using the NNETAR() function in the fable R package", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", "end_datetime": "2024-06-03T00:00:00Z", "providers": [ @@ -58,8 +58,8 @@ "Temp_C_mean", "Daily", "P1D", - "fcre", "bvre", + "fcre", "machine learning" ], "table:columns": [ diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/fableNNETAR_focal.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/fableNNETAR_focal.json index 008158f33..61e5a24db 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/fableNNETAR_focal.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/fableNNETAR_focal.json @@ -27,9 +27,9 @@ "properties": { "title": "fableNNETAR_focal", "description": "autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-04-26T00:00:00Z", - "end_datetime": "2024-10-09T00:00:00Z", + "end_datetime": "2024-10-10T00:00:00Z", "providers": [ { "url": "https://github.com/addelany/vera4casts/blob/main/code/combined_workflow/nnetar_workflow.R", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/flareGOTM.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/flareGOTM.json index da60ab651..7b72a7036 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/flareGOTM.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/flareGOTM.json @@ -23,9 +23,9 @@ "properties": { "title": "flareGOTM", "description": "FLARE-GOTM combines the 1D hydrodynamic process-based model GOTM, a data assimilation algorithm, and NOAA weather data to forecast water column temperatures.", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-01-21T00:00:00Z", - "end_datetime": "2024-10-04T00:00:00Z", + "end_datetime": "2024-10-05T00:00:00Z", "providers": [ { "url": null, diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/flareSimstrat.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/flareSimstrat.json index 6b8892799..f0616639e 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/flareSimstrat.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/flareSimstrat.json @@ -23,9 +23,9 @@ "properties": { "title": "flareSimstrat", "description": "FLARE-Simstrat combines the 1D process-based model Simstrat, a data assimilation algorithm (EnKF) and NOAA driver weather data to make predictions of water column temperatures.", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-01-21T00:00:00Z", - "end_datetime": "2024-10-04T00:00:00Z", + "end_datetime": "2024-10-05T00:00:00Z", "providers": [ { "url": "https://github.com/FLARE-forecast/FCRE-forecast-code/blob/main/workflows/ler/combined_workflow_Simstrat.R", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/glm_aed_v1.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/glm_aed_v1.json index 16e8e41d2..33d9ca42d 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/glm_aed_v1.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/glm_aed_v1.json @@ -23,7 +23,7 @@ "properties": { "title": "glm_aed_v1", "description": "GLM-AED with Ensemble Kalman Filter as implemented in FLARE. This version used DA to update model states but not model parameters.", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-14T00:00:00Z", "end_datetime": "2024-10-05T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/historic_mean.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/historic_mean.json index fe96a447c..969c4f109 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/historic_mean.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/historic_mean.json @@ -31,9 +31,9 @@ "properties": { "title": "historic_mean", "description": "Calculates the mean state from the historic timeseries and applies this to the forecast horizon. The model uses the fable R package MEAN() function to fit this model, with the uncertainty generated from the residuals of the fitted model.", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-08T00:00:00Z", + "end_datetime": "2024-10-09T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/fableMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/inflow_gefsClimAED.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/inflow_gefsClimAED.json index eb089927c..3ab7ff93b 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/inflow_gefsClimAED.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/inflow_gefsClimAED.json @@ -23,9 +23,9 @@ "properties": { "title": "inflow_gefsClimAED", "description": "flow is forecasted as using a linear relationship between historical flow, month, and 5-day sum of precipitation. Temperature is forecasted using a linear relationship between historical water temperature, month, and 5-day mean air temperature. NOAA GEFS is then used to get the future values of 5-day sum precipitation and mean temperature. Nutrients are forecasting using the DOY climatology. The DOY climatology was developed using a linear interpolation of the historical samples.", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-10-13T00:00:00Z", - "end_datetime": "2024-10-06T00:00:00Z", + "end_datetime": "2024-10-07T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast_models/blob/main/inflow_aed.R", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/monthly_mean.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/monthly_mean.json index ac338f0e0..12babb5be 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/monthly_mean.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/monthly_mean.json @@ -31,9 +31,9 @@ "properties": { "title": "monthly_mean", "description": "This model calculates a monthly mean from the historic data and assigns this as the mean prediction for any day within that month. The standard deviation of the observations for that month is given as the standard deviation of the forecast.", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2024-02-06T00:00:00Z", - "end_datetime": "2024-10-10T00:00:00Z", + "end_datetime": "2024-10-11T00:00:00Z", "providers": [ { "url": "https://github.com/OlssonF/vera4cast/blob/main/R/MonthlyMeanModelFunction.R", diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/persistenceFO.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/persistenceFO.json index 459523485..ce12f5a17 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/persistenceFO.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/persistenceFO.json @@ -23,7 +23,7 @@ "properties": { "title": "persistenceFO", "description": "another persistence forecast", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-09-27T00:00:00Z", "end_datetime": "2023-10-30T00:00:00Z", "providers": [ diff --git a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/persistenceRW.json b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/persistenceRW.json index a2c4af816..3fade3133 100644 --- a/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/persistenceRW.json +++ b/data/challenge/vera4cast-stac/summaries/Physical/Daily_Water_temperature/models/persistenceRW.json @@ -31,9 +31,9 @@ "properties": { "title": "persistenceRW", "description": "Random walk from the fable package with ensembles used to represent uncertainty", - "datetime": "2024-09-05T00:00:00Z", + "datetime": "2024-09-06T00:00:00Z", "start_datetime": "2023-09-21T00:00:00Z", - "end_datetime": "2024-10-08T00:00:00Z", + "end_datetime": "2024-10-09T00:00:00Z", "providers": [ { "url": "https://github.com/LTREB-reservoirs/vera4cast/blob/main/models/run_terrestrial_baselines.R", diff --git a/data/challenge/vera4cast-stac/targets/collection.json b/data/challenge/vera4cast-stac/targets/collection.json index 175ed7b58..8c6b806e3 100644 --- a/data/challenge/vera4cast-stac/targets/collection.json +++ b/data/challenge/vera4cast-stac/targets/collection.json @@ -58,7 +58,7 @@ "interval": [ [ "2013-03-07T00:00:00Z", - "2024-09-04T00:00:00Z" + "2024-09-05T00:00:00Z" ] ] } diff --git a/data/output/neon4cast-stac/c0e44bd418.json b/data/output/neon4cast-stac/002c9b32e5.json similarity index 97% rename from data/output/neon4cast-stac/c0e44bd418.json rename to data/output/neon4cast-stac/002c9b32e5.json index 8c7476471..9ead26f60 100644 --- a/data/output/neon4cast-stac/c0e44bd418.json +++ b/data/output/neon4cast-stac/002c9b32e5.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:66e0c4ba3c", "name": "tg_arima_le_P1D_summaries summaries", - "description": "All summaries for the Daily_latent_heat_flux variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_latent_heat_flux variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,6 +16,21 @@ "le", "Daily", "P1D", + "LAJA", + "LENO", + "MLBS", + "MOAB", + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER", "SOAP", "SRER", "STEI", @@ -47,22 +62,7 @@ "JERC", "JORN", "KONA", - "KONZ", - "LAJA", - "LENO", - "MLBS", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER" + "KONZ" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/a5071d4771.json b/data/output/neon4cast-stac/0da51b5517.json similarity index 97% rename from data/output/neon4cast-stac/a5071d4771.json rename to data/output/neon4cast-stac/0da51b5517.json index fdb24b598..4ebbd500b 100644 --- a/data/output/neon4cast-stac/a5071d4771.json +++ b/data/output/neon4cast-stac/0da51b5517.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:0623758b24", "name": "tg_tbats_nee_P1D_scores scores", - "description": "All scores for the Daily_Net_ecosystem_exchange variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Net_ecosystem_exchange variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,6 +16,19 @@ "nee", "Daily", "P1D", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -49,20 +62,7 @@ "RMNP", "SCBI", "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "SJER" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/eb12c65609.json b/data/output/neon4cast-stac/0ddb9eaa22.json similarity index 96% rename from data/output/neon4cast-stac/eb12c65609.json rename to data/output/neon4cast-stac/0ddb9eaa22.json index 8130123d6..5830d4540 100644 --- a/data/output/neon4cast-stac/eb12c65609.json +++ b/data/output/neon4cast-stac/0ddb9eaa22.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:b66262dba9", "name": "climatology_le_PT30M_summaries summaries", - "description": "All summaries for the 30min_latent_heat_flux variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: SJER, SOAP, SRER, STEI, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, STER, TALL, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the 30min_latent_heat_flux variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, SCBI, RMNP, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,10 +16,13 @@ "le", "30min", "PT30M", + "SERC", "SJER", "SOAP", "SRER", "STEI", + "STER", + "TALL", "TEAK", "TOOL", "TREE", @@ -31,8 +34,6 @@ "ABBY", "BARR", "BART", - "STER", - "TALL", "BLAN", "BONA", "CLBJ", @@ -50,6 +51,8 @@ "KONA", "KONZ", "LAJA", + "SCBI", + "RMNP", "LENO", "MLBS", "MOAB", @@ -59,10 +62,7 @@ "ONAQ", "ORNL", "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC" + "PUUM" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/f05befad20.json b/data/output/neon4cast-stac/0de889ff04.json similarity index 98% rename from data/output/neon4cast-stac/f05befad20.json rename to data/output/neon4cast-stac/0de889ff04.json index 2c202f9e4..a07c10f09 100644 --- a/data/output/neon4cast-stac/f05befad20.json +++ b/data/output/neon4cast-stac/0de889ff04.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:bcec4bf7bf", "name": "flareGLM_noDA_temperature_P1D_scores scores", - "description": "All scores for the Daily_Water_temperature variable for the flareGLM_noDA model. Information for the model is provided as follows: The FLARE-GLM is a forecasting framework that integrates the General Lake Model\nhydrodynamic process model (GLM; Hipsey et al., 2019). This version does not incorportate data assimilation.\n The model predicts this variable at the following sites: BARC, CRAM, LIRO, PRLA, PRPO, SUGG, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Water_temperature variable for the flareGLM_noDA model. Information for the model is provided as follows: The FLARE-GLM is a forecasting framework that integrates the General Lake Model\nhydrodynamic process model (GLM; Hipsey et al., 2019). This version does not incorportate data assimilation.\n The model predicts this variable at the following sites: PRLA, PRPO, SUGG, TOOK, BARC, CRAM, LIRO.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,13 +16,13 @@ "temperature", "Daily", "P1D", - "BARC", - "CRAM", - "LIRO", "PRLA", "PRPO", "SUGG", - "TOOK" + "TOOK", + "BARC", + "CRAM", + "LIRO" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/7d2ead0125.json b/data/output/neon4cast-stac/13227c9a0a.json similarity index 98% rename from data/output/neon4cast-stac/7d2ead0125.json rename to data/output/neon4cast-stac/13227c9a0a.json index c1aff6fc2..6820e19ed 100644 --- a/data/output/neon4cast-stac/7d2ead0125.json +++ b/data/output/neon4cast-stac/13227c9a0a.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:38a85021ba", "name": "baseline_ensemble_temperature_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Water_temperature variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: MCDI, MCRA, POSE, PRIN, REDB, SUGG, HOPB, KING, LECO, LEWI, MART, MAYF, SYCA, TECR, TOMB, WALK, WLOU, BLWA, COMO, CUPE, FLNT, GUIL, ARIK, BARC, BIGC, BLDE, BLUE, CRAM, LIRO, PRLA, PRPO, CARI, OKSR, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Water_temperature variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: POSE, PRIN, REDB, SUGG, MCDI, MCRA, HOPB, KING, LECO, LEWI, MART, MAYF, SYCA, TECR, TOMB, WALK, WLOU, BLWA, COMO, CUPE, FLNT, GUIL, ARIK, BARC, BIGC, BLDE, BLUE, CRAM, LIRO, CARI, PRLA, PRPO, OKSR, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,12 +16,12 @@ "temperature", "Daily", "P1D", - "MCDI", - "MCRA", "POSE", "PRIN", "REDB", "SUGG", + "MCDI", + "MCRA", "HOPB", "KING", "LECO", @@ -45,9 +45,9 @@ "BLUE", "CRAM", "LIRO", + "CARI", "PRLA", "PRPO", - "CARI", "OKSR", "TOOK" ], diff --git a/data/output/neon4cast-stac/b2b689d95d.json b/data/output/neon4cast-stac/17ff2cf74e.json similarity index 98% rename from data/output/neon4cast-stac/b2b689d95d.json rename to data/output/neon4cast-stac/17ff2cf74e.json index 03c6bcb50..c91e16643 100644 --- a/data/output/neon4cast-stac/b2b689d95d.json +++ b/data/output/neon4cast-stac/17ff2cf74e.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:5458e6b959", "name": "tg_arima_amblyomma_americanum_P1W_forecast forecasts", - "description": "All forecasts for the Weekly_Amblyomma_americanum_population variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ORNL, OSBS, SCBI, SERC, TALL, UKFS, BLAN, KONZ, LENO.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Weekly_Amblyomma_americanum_population variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: BLAN, KONZ, LENO, ORNL, OSBS, SCBI, SERC, TALL, UKFS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,15 +16,15 @@ "amblyomma_americanum", "Weekly", "P1W", + "BLAN", + "KONZ", + "LENO", "ORNL", "OSBS", "SCBI", "SERC", "TALL", - "UKFS", - "BLAN", - "KONZ", - "LENO" + "UKFS" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/8b77308b4d.json b/data/output/neon4cast-stac/192bf4c203.json similarity index 96% rename from data/output/neon4cast-stac/8b77308b4d.json rename to data/output/neon4cast-stac/192bf4c203.json index 11410c8dc..19210c44d 100644 --- a/data/output/neon4cast-stac/8b77308b4d.json +++ b/data/output/neon4cast-stac/192bf4c203.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:8c838ef1c9", "name": "tg_arima_le_P1D_forecast forecasts", - "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,19 +16,6 @@ "le", "Daily", "P1D", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -62,7 +49,20 @@ "RMNP", "SCBI", "SERC", - "SJER" + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/fcf8629dac.json b/data/output/neon4cast-stac/1b3917171e.json similarity index 96% rename from data/output/neon4cast-stac/fcf8629dac.json rename to data/output/neon4cast-stac/1b3917171e.json index 0a68f67f4..3df8b89ba 100644 --- a/data/output/neon4cast-stac/fcf8629dac.json +++ b/data/output/neon4cast-stac/1b3917171e.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:a8d2068a7b", "name": "tg_randfor_richness_P1W_forecast forecasts", - "description": "All forecasts for the Weekly_beetle_community_richness variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Weekly_beetle_community_richness variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,22 +16,6 @@ "richness", "Weekly", "P1W", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", "JORN", "KONA", "KONZ", @@ -62,7 +46,23 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/af591151c2.json b/data/output/neon4cast-stac/1c8d6c38f0.json similarity index 99% rename from data/output/neon4cast-stac/af591151c2.json rename to data/output/neon4cast-stac/1c8d6c38f0.json index ba5ae855b..2329aaf1b 100644 --- a/data/output/neon4cast-stac/af591151c2.json +++ b/data/output/neon4cast-stac/1c8d6c38f0.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:65f97f5ad2", "name": "bee_bake_RFModel_2024_temperature_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Water_temperature variable for the bee_bake_RFModel_2024 model. Information for the model is provided as follows: Random Forest.\n The model predicts this variable at the following sites: CRAM, BARC, PRLA, SUGG, PRPO, LIRO, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Water_temperature variable for the bee_bake_RFModel_2024 model. Information for the model is provided as follows: Random Forest.\n The model predicts this variable at the following sites: LIRO, PRPO, BARC, CRAM, PRLA, SUGG, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,12 +16,12 @@ "temperature", "Daily", "P1D", - "CRAM", + "LIRO", + "PRPO", "BARC", + "CRAM", "PRLA", "SUGG", - "PRPO", - "LIRO", "TOOK" ], "citation": { diff --git a/data/output/neon4cast-stac/e0cbc91be4.json b/data/output/neon4cast-stac/1cf3bcbed7.json similarity index 97% rename from data/output/neon4cast-stac/e0cbc91be4.json rename to data/output/neon4cast-stac/1cf3bcbed7.json index b4864eb00..fa236ab75 100644 --- a/data/output/neon4cast-stac/e0cbc91be4.json +++ b/data/output/neon4cast-stac/1cf3bcbed7.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:3493533768", "name": "persistenceRW_temperature_P1D_scores scores", - "description": "All scores for the Daily_Water_temperature variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Water_temperature variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,12 +16,6 @@ "temperature", "Daily", "P1D", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", - "BLWA", "CARI", "COMO", "CRAM", @@ -49,7 +43,13 @@ "TOMB", "TOOK", "WALK", - "WLOU" + "WLOU", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", + "BLWA" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/da92264c95.json b/data/output/neon4cast-stac/1ee1ddbe68.json similarity index 97% rename from data/output/neon4cast-stac/da92264c95.json rename to data/output/neon4cast-stac/1ee1ddbe68.json index d612bf586..47dd5c67e 100644 --- a/data/output/neon4cast-stac/da92264c95.json +++ b/data/output/neon4cast-stac/1ee1ddbe68.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:a83dc80d0e", "name": "baseline_ensemble_rcc_90_P1D_scores scores", - "description": "All scores for the Daily_Red_chromatic_coordinate variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Red_chromatic_coordinate variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,9 +16,6 @@ "rcc_90", "Daily", "P1D", - "ABBY", - "BARR", - "BART", "BLAN", "BONA", "CLBJ", @@ -62,7 +59,10 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/f6e4a76c0b.json b/data/output/neon4cast-stac/20fca17d08.json similarity index 97% rename from data/output/neon4cast-stac/f6e4a76c0b.json rename to data/output/neon4cast-stac/20fca17d08.json index ffb1ba0ca..0bc9b1aec 100644 --- a/data/output/neon4cast-stac/f6e4a76c0b.json +++ b/data/output/neon4cast-stac/20fca17d08.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:4abc30e762", "name": "tg_tbats_oxygen_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Dissolved_oxygen variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Dissolved_oxygen variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,16 +16,6 @@ "oxygen", "Daily", "P1D", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", - "BLWA", - "CARI", - "COMO", - "CRAM", - "CUPE", "FLNT", "GUIL", "HOPB", @@ -49,7 +39,17 @@ "TOMB", "TOOK", "WALK", - "WLOU" + "WLOU", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", + "BLWA", + "CARI", + "COMO", + "CRAM", + "CUPE" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/b67fe46dbd.json b/data/output/neon4cast-stac/295f186042.json similarity index 97% rename from data/output/neon4cast-stac/b67fe46dbd.json rename to data/output/neon4cast-stac/295f186042.json index ba6575f6e..45a391713 100644 --- a/data/output/neon4cast-stac/b67fe46dbd.json +++ b/data/output/neon4cast-stac/295f186042.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:1a8f7250ca", "name": "tg_ets_oxygen_P1D_scores scores", - "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,6 +16,20 @@ "oxygen", "Daily", "P1D", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", + "BLWA", + "CARI", + "COMO", + "CRAM", + "CUPE", + "FLNT", + "GUIL", + "HOPB", + "KING", "LECO", "LEWI", "LIRO", @@ -35,21 +49,7 @@ "TOMB", "TOOK", "WALK", - "WLOU", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", - "BLWA", - "CARI", - "COMO", - "CRAM", - "CUPE", - "FLNT", - "GUIL", - "HOPB", - "KING" + "WLOU" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/a218348d41.json b/data/output/neon4cast-stac/2ebd9ad2e0.json similarity index 96% rename from data/output/neon4cast-stac/a218348d41.json rename to data/output/neon4cast-stac/2ebd9ad2e0.json index a58d649de..06b05422f 100644 --- a/data/output/neon4cast-stac/a218348d41.json +++ b/data/output/neon4cast-stac/2ebd9ad2e0.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:e35366e083", "name": "tg_tbats_le_P1D_forecast forecasts", - "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,28 +16,6 @@ "le", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA", - "KONZ", - "LAJA", - "LENO", - "MLBS", "MOAB", "NIWO", "NOGP", @@ -62,7 +40,29 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", + "KONA", + "KONZ", + "LAJA", + "LENO", + "MLBS" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/36123a2f6d.json b/data/output/neon4cast-stac/2f0d29ccea.json similarity index 97% rename from data/output/neon4cast-stac/36123a2f6d.json rename to data/output/neon4cast-stac/2f0d29ccea.json index 2d80586d6..32c400580 100644 --- a/data/output/neon4cast-stac/36123a2f6d.json +++ b/data/output/neon4cast-stac/2f0d29ccea.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:b392c65b4d", "name": "climatology_temperature_P1D_scores scores", - "description": "All scores for the Daily_Water_temperature variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: TECR, TOMB, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Water_temperature variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,10 +16,6 @@ "temperature", "Daily", "P1D", - "TECR", - "TOMB", - "WALK", - "WLOU", "ARIK", "BARC", "BIGC", @@ -49,6 +45,10 @@ "REDB", "SUGG", "SYCA", + "TECR", + "TOMB", + "WALK", + "WLOU", "TOOK" ], "citation": { diff --git a/data/output/neon4cast-stac/ddbdd8e934.json b/data/output/neon4cast-stac/30faf9501d.json similarity index 96% rename from data/output/neon4cast-stac/ddbdd8e934.json rename to data/output/neon4cast-stac/30faf9501d.json index b86d1a628..6e7ab4ef5 100644 --- a/data/output/neon4cast-stac/ddbdd8e934.json +++ b/data/output/neon4cast-stac/30faf9501d.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:9fae5a1d6a", "name": "tg_arima_nee_P1D_scores scores", - "description": "All scores for the Daily_Net_ecosystem_exchange variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Net_ecosystem_exchange variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,22 +16,6 @@ "nee", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", "JORN", "KONA", "KONZ", @@ -62,7 +46,23 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/08d1870d1b.json b/data/output/neon4cast-stac/31a615d2f9.json similarity index 97% rename from data/output/neon4cast-stac/08d1870d1b.json rename to data/output/neon4cast-stac/31a615d2f9.json index 2a0bfbe44..77624c704 100644 --- a/data/output/neon4cast-stac/08d1870d1b.json +++ b/data/output/neon4cast-stac/31a615d2f9.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:69761cdbfe", "name": "tg_tbats_gcc_90_P1D_scores scores", - "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,6 +16,10 @@ "gcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", "BONA", "CLBJ", "CPER", @@ -58,11 +62,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN" + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/bacce1138d.json b/data/output/neon4cast-stac/357a3a74a7.json similarity index 96% rename from data/output/neon4cast-stac/bacce1138d.json rename to data/output/neon4cast-stac/357a3a74a7.json index a8df730a5..69701965a 100644 --- a/data/output/neon4cast-stac/bacce1138d.json +++ b/data/output/neon4cast-stac/357a3a74a7.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:fba484c7c6", "name": "tg_arima_abundance_P1W_scores scores", - "description": "All scores for the Weekly_beetle_community_abundance variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Weekly_beetle_community_abundance variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,11 +16,6 @@ "abundance", "Weekly", "P1W", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", "CLBJ", "CPER", "DCFS", @@ -62,7 +57,12 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/3e76d386ad.json b/data/output/neon4cast-stac/36dc70b107.json similarity index 99% rename from data/output/neon4cast-stac/3e76d386ad.json rename to data/output/neon4cast-stac/36dc70b107.json index 412f1b662..8fdf0a590 100644 --- a/data/output/neon4cast-stac/3e76d386ad.json +++ b/data/output/neon4cast-stac/36dc70b107.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:f6a8f8b75e", "name": "baseline_ensemble_rcc_90_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, SERC, SJER, SOAP, SRER, STEI, UNDE, WOOD, WREF, YELL, STER, TALL, TEAK, TREE, UKFS, DELA, DSNY, GRSM, GUAN, MLBS, MOAB, NIWO, NOGP, TOOL, BONA, DEJU, HEAL, BARR.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, SERC, SJER, SOAP, SRER, STEI, UNDE, WOOD, WREF, YELL, STER, TALL, TEAK, TREE, UKFS, DELA, DSNY, GRSM, GUAN, MLBS, MOAB, NIWO, NOGP, BONA, DEJU, HEAL, BARR, TOOL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -58,11 +58,11 @@ "MOAB", "NIWO", "NOGP", - "TOOL", "BONA", "DEJU", "HEAL", - "BARR" + "BARR", + "TOOL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/1c2d2a7888.json b/data/output/neon4cast-stac/404ed9404a.json similarity index 99% rename from data/output/neon4cast-stac/1c2d2a7888.json rename to data/output/neon4cast-stac/404ed9404a.json index dd7326ab9..8a47d1b6f 100644 --- a/data/output/neon4cast-stac/1c2d2a7888.json +++ b/data/output/neon4cast-stac/404ed9404a.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:1a3e66d93f", "name": "baseline_ensemble_rcc_90_P1D_summaries summaries", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, SERC, SJER, SOAP, SRER, STEI, UNDE, WOOD, WREF, YELL, STER, TALL, TEAK, TREE, UKFS, DELA, DSNY, GRSM, GUAN, MLBS, MOAB, NIWO, NOGP, BONA, DEJU, HEAL, TOOL, BARR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, SERC, SJER, SOAP, SRER, STEI, UNDE, WOOD, WREF, YELL, STER, TALL, TEAK, TREE, UKFS, DELA, DSNY, GRSM, GUAN, MLBS, MOAB, NIWO, NOGP, BARR, BONA, DEJU, HEAL, TOOL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -58,11 +58,11 @@ "MOAB", "NIWO", "NOGP", + "BARR", "BONA", "DEJU", "HEAL", - "TOOL", - "BARR" + "TOOL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/f9d1dca977.json b/data/output/neon4cast-stac/432e9e9a6a.json similarity index 96% rename from data/output/neon4cast-stac/f9d1dca977.json rename to data/output/neon4cast-stac/432e9e9a6a.json index 6b66dd692..33a130983 100644 --- a/data/output/neon4cast-stac/f9d1dca977.json +++ b/data/output/neon4cast-stac/432e9e9a6a.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:cd0e2b13d1", "name": "tg_ets_richness_P1W_forecast forecasts", - "description": "All forecasts for the Weekly_beetle_community_richness variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Weekly_beetle_community_richness variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,25 +16,6 @@ "richness", "Weekly", "P1W", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -62,7 +43,26 @@ "NOGP", "OAES", "ONAQ", - "ORNL" + "ORNL", + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/671d93aa92.json b/data/output/neon4cast-stac/47ccfd8ef7.json similarity index 96% rename from data/output/neon4cast-stac/671d93aa92.json rename to data/output/neon4cast-stac/47ccfd8ef7.json index f8d88e28a..0c380ddf2 100644 --- a/data/output/neon4cast-stac/671d93aa92.json +++ b/data/output/neon4cast-stac/47ccfd8ef7.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:fbde1ff371", "name": "tg_lasso_rcc_90_P1D_summaries summaries", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,22 +16,6 @@ "rcc_90", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", "JORN", "KONA", "KONZ", @@ -62,7 +46,23 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/60ff9b0093.json b/data/output/neon4cast-stac/47f945ead0.json similarity index 96% rename from data/output/neon4cast-stac/60ff9b0093.json rename to data/output/neon4cast-stac/47f945ead0.json index acb492944..a84ead6d1 100644 --- a/data/output/neon4cast-stac/60ff9b0093.json +++ b/data/output/neon4cast-stac/47f945ead0.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:615ebe648a", "name": "tg_tbats_nee_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,6 +16,25 @@ "nee", "Daily", "P1D", + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -43,26 +62,7 @@ "NOGP", "OAES", "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "ORNL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/39acc6377a.json b/data/output/neon4cast-stac/4bb01a9795.json similarity index 97% rename from data/output/neon4cast-stac/39acc6377a.json rename to data/output/neon4cast-stac/4bb01a9795.json index e499fffd8..d55bb4233 100644 --- a/data/output/neon4cast-stac/39acc6377a.json +++ b/data/output/neon4cast-stac/4bb01a9795.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:f7e09332fe", "name": "tg_precip_lm_le_P1D_summaries summaries", - "description": "All summaries for the Daily_latent_heat_flux variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_latent_heat_flux variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,21 +16,6 @@ "le", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", "JERC", "JORN", "KONA", @@ -62,7 +47,22 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/5d46bfea09.json b/data/output/neon4cast-stac/4bc72f7388.json similarity index 96% rename from data/output/neon4cast-stac/5d46bfea09.json rename to data/output/neon4cast-stac/4bc72f7388.json index f16fc4480..c9a379c53 100644 --- a/data/output/neon4cast-stac/5d46bfea09.json +++ b/data/output/neon4cast-stac/4bc72f7388.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:ef4a9d268c", "name": "tg_precip_lm_all_sites_rcc_90_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,6 +16,18 @@ "rcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", "GUAN", "HARV", "HEAL", @@ -50,19 +62,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM" + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/ddc926554f.json b/data/output/neon4cast-stac/4c9db03d88.json similarity index 97% rename from data/output/neon4cast-stac/ddc926554f.json rename to data/output/neon4cast-stac/4c9db03d88.json index ed0592f88..1dfc7a738 100644 --- a/data/output/neon4cast-stac/ddc926554f.json +++ b/data/output/neon4cast-stac/4c9db03d88.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:00ca965f81", "name": "tg_temp_lm_gcc_90_P1D_summaries summaries", - "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,6 +16,22 @@ "gcc_90", "Daily", "P1D", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -46,23 +62,7 @@ "ORNL", "OSBS", "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "RMNP" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/da442ea7f8.json b/data/output/neon4cast-stac/4d2b2a1612.json similarity index 97% rename from data/output/neon4cast-stac/da442ea7f8.json rename to data/output/neon4cast-stac/4d2b2a1612.json index 73fbf858a..2ac46bf49 100644 --- a/data/output/neon4cast-stac/da442ea7f8.json +++ b/data/output/neon4cast-stac/4d2b2a1612.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:8b853b3e5a", "name": "tg_ets_rcc_90_P1D_summaries summaries", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,6 +16,23 @@ "rcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", "KONA", "KONZ", "LAJA", @@ -45,24 +62,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN" + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/26b9ced9ef.json b/data/output/neon4cast-stac/4d8a71c555.json similarity index 97% rename from data/output/neon4cast-stac/26b9ced9ef.json rename to data/output/neon4cast-stac/4d8a71c555.json index 77e5cb0be..ac8fe691c 100644 --- a/data/output/neon4cast-stac/26b9ced9ef.json +++ b/data/output/neon4cast-stac/4d8a71c555.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:7ffe793c7b", "name": "tg_tbats_rcc_90_P1D_summaries summaries", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,10 +16,6 @@ "rcc_90", "Daily", "P1D", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -62,7 +58,11 @@ "TEAK", "TOOL", "TREE", - "UKFS" + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/b09525783a.json b/data/output/neon4cast-stac/4f2a8ee3fc.json similarity index 98% rename from data/output/neon4cast-stac/b09525783a.json rename to data/output/neon4cast-stac/4f2a8ee3fc.json index e0e277587..6113e22ea 100644 --- a/data/output/neon4cast-stac/b09525783a.json +++ b/data/output/neon4cast-stac/4f2a8ee3fc.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:e7e4d0b48d", "name": "tg_tbats_chla_P1D_scores scores", - "description": "All scores for the Daily_Chlorophyll_a variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: SUGG, TOMB, TOOK, BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Chlorophyll_a variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,16 +16,16 @@ "chla", "Daily", "P1D", - "SUGG", - "TOMB", - "TOOK", "BARC", "BLWA", "CRAM", "FLNT", "LIRO", "PRLA", - "PRPO" + "PRPO", + "SUGG", + "TOMB", + "TOOK" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/ea1bdea521.json b/data/output/neon4cast-stac/4f7d2fdf64.json similarity index 97% rename from data/output/neon4cast-stac/ea1bdea521.json rename to data/output/neon4cast-stac/4f7d2fdf64.json index e842891b4..560333e77 100644 --- a/data/output/neon4cast-stac/ea1bdea521.json +++ b/data/output/neon4cast-stac/4f7d2fdf64.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:940dc24ebd", "name": "tg_ets_le_P1D_scores scores", - "description": "All scores for the Daily_latent_heat_flux variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_latent_heat_flux variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,20 +16,6 @@ "le", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", "HEAL", "JERC", "JORN", @@ -62,7 +48,21 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/2835e8eb34.json b/data/output/neon4cast-stac/50ce8830a1.json similarity index 96% rename from data/output/neon4cast-stac/2835e8eb34.json rename to data/output/neon4cast-stac/50ce8830a1.json index d480941ba..3c1df69ea 100644 --- a/data/output/neon4cast-stac/2835e8eb34.json +++ b/data/output/neon4cast-stac/50ce8830a1.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:1354e5c7e8", "name": "tg_arima_rcc_90_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,14 +16,6 @@ "rcc_90", "Daily", "P1D", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", "DELA", "DSNY", "GRSM", @@ -62,7 +54,15 @@ "WOOD", "WREF", "YELL", - "ABBY" + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/5d6b407bc6.json b/data/output/neon4cast-stac/5206ef36c9.json similarity index 96% rename from data/output/neon4cast-stac/5d6b407bc6.json rename to data/output/neon4cast-stac/5206ef36c9.json index 26cbf91d3..9718b1506 100644 --- a/data/output/neon4cast-stac/5d6b407bc6.json +++ b/data/output/neon4cast-stac/5206ef36c9.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:0ce2955e76", "name": "tg_precip_lm_rcc_90_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,21 +16,6 @@ "rcc_90", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", "JERC", "JORN", "KONA", @@ -62,7 +47,22 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/281065ce6a.json b/data/output/neon4cast-stac/52987ded66.json similarity index 97% rename from data/output/neon4cast-stac/281065ce6a.json rename to data/output/neon4cast-stac/52987ded66.json index 824376401..df77dcfd9 100644 --- a/data/output/neon4cast-stac/281065ce6a.json +++ b/data/output/neon4cast-stac/52987ded66.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:a528091491", "name": "climatology_oxygen_P1D_summaries summaries", - "description": "All summaries for the Daily_Dissolved_oxygen variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: REDB, SUGG, SYCA, TECR, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, TOMB, BLWA, CRAM, CARI, LIRO, PRPO, PRLA, TOOK, OKSR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Dissolved_oxygen variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: BLWA, CARI, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, TOMB, CRAM, LIRO, PRPO, PRLA, TOOK, OKSR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,17 +16,8 @@ "oxygen", "Daily", "P1D", - "REDB", - "SUGG", - "SYCA", - "TECR", - "WALK", - "WLOU", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", + "BLWA", + "CARI", "COMO", "CUPE", "FLNT", @@ -41,10 +32,19 @@ "MCRA", "POSE", "PRIN", + "REDB", + "SUGG", + "SYCA", + "TECR", + "WALK", + "WLOU", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", "TOMB", - "BLWA", "CRAM", - "CARI", "LIRO", "PRPO", "PRLA", diff --git a/data/output/neon4cast-stac/7233a9d37d.json b/data/output/neon4cast-stac/573b1a20f8.json similarity index 97% rename from data/output/neon4cast-stac/7233a9d37d.json rename to data/output/neon4cast-stac/573b1a20f8.json index 3dad693ca..925b9d880 100644 --- a/data/output/neon4cast-stac/7233a9d37d.json +++ b/data/output/neon4cast-stac/573b1a20f8.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:6a10d4165e", "name": "air2waterSat_2_temperature_P1D_summaries summaries", - "description": "All summaries for the Daily_Water_temperature variable for the air2waterSat_2 model. Information for the model is provided as follows: The air2water model is a linear model fit using the function lm() in R and uses air temperature as\na covariate.\n The model predicts this variable at the following sites: TOOK, WALK, WLOU, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Water_temperature variable for the air2waterSat_2 model. Information for the model is provided as follows: The air2water model is a linear model fit using the function lm() in R and uses air temperature as\na covariate.\n The model predicts this variable at the following sites: LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, TOOK, WALK, WLOU.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,9 +16,7 @@ "temperature", "Daily", "P1D", - "TOOK", - "WALK", - "WLOU", + "LEWI", "LIRO", "MART", "MAYF", @@ -49,7 +47,9 @@ "HOPB", "KING", "LECO", - "LEWI" + "TOOK", + "WALK", + "WLOU" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/b2c513fe74.json b/data/output/neon4cast-stac/587cd8a8ed.json similarity index 97% rename from data/output/neon4cast-stac/b2c513fe74.json rename to data/output/neon4cast-stac/587cd8a8ed.json index b13939ca1..a3138e4ca 100644 --- a/data/output/neon4cast-stac/b2c513fe74.json +++ b/data/output/neon4cast-stac/587cd8a8ed.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:5e7e8f8240", "name": "persistenceRW_rcc_90_P1D_scores scores", - "description": "All scores for the Daily_Red_chromatic_coordinate variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Red_chromatic_coordinate variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,6 +16,20 @@ "rcc_90", "Daily", "P1D", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -48,21 +62,7 @@ "PUUM", "RMNP", "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "SERC" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/d8176111f8.json b/data/output/neon4cast-stac/599c7721f5.json similarity index 96% rename from data/output/neon4cast-stac/d8176111f8.json rename to data/output/neon4cast-stac/599c7721f5.json index 9bae7a4b7..f59538c72 100644 --- a/data/output/neon4cast-stac/d8176111f8.json +++ b/data/output/neon4cast-stac/599c7721f5.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:c78386b201", "name": "tg_arima_nee_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Net_ecosystem_exchange variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,6 +16,24 @@ "nee", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", + "KONA", "KONZ", "LAJA", "LENO", @@ -44,25 +62,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA" + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/3339b872b3.json b/data/output/neon4cast-stac/5b863dc9b5.json similarity index 96% rename from data/output/neon4cast-stac/3339b872b3.json rename to data/output/neon4cast-stac/5b863dc9b5.json index 0ab7311b5..504b70231 100644 --- a/data/output/neon4cast-stac/3339b872b3.json +++ b/data/output/neon4cast-stac/5b863dc9b5.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:812c245286", "name": "tg_precip_lm_le_P1D_forecast forecasts", - "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_precip_lm model. Information for the model is provided as follows: The tg_precip_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only total precipitation used as a model covariate..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,6 +16,21 @@ "le", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", "JERC", "JORN", "KONA", @@ -47,22 +62,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL" + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/7d41fcf814.json b/data/output/neon4cast-stac/5df0b520a2.json similarity index 97% rename from data/output/neon4cast-stac/7d41fcf814.json rename to data/output/neon4cast-stac/5df0b520a2.json index 55e4d260b..3fa725754 100644 --- a/data/output/neon4cast-stac/7d41fcf814.json +++ b/data/output/neon4cast-stac/5df0b520a2.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:f2a39857ea", "name": "climatology_oxygen_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Dissolved_oxygen variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, WALK, WLOU, TOMB, BLWA, CRAM, CARI, LIRO, PRPO, PRLA, TOOK, OKSR.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Dissolved_oxygen variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: BLWA, CARI, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, TOMB, CRAM, LIRO, PRPO, PRLA, TOOK, OKSR.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,11 +16,8 @@ "oxygen", "Daily", "P1D", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", + "BLWA", + "CARI", "COMO", "CUPE", "FLNT", @@ -41,10 +38,13 @@ "TECR", "WALK", "WLOU", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", "TOMB", - "BLWA", "CRAM", - "CARI", "LIRO", "PRPO", "PRLA", diff --git a/data/output/neon4cast-stac/a3f839db1e.json b/data/output/neon4cast-stac/6052df7e34.json similarity index 97% rename from data/output/neon4cast-stac/a3f839db1e.json rename to data/output/neon4cast-stac/6052df7e34.json index b40722eea..33f08e45f 100644 --- a/data/output/neon4cast-stac/a3f839db1e.json +++ b/data/output/neon4cast-stac/6052df7e34.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:51bb542a22", "name": "tg_precip_lm_all_sites_oxygen_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Dissolved_oxygen variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Dissolved_oxygen variable for the tg_precip_lm_all_sites model. Information for the model is provided as follows: The tg_precip_lm_all_sites model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation. y. This model was used to forecast water temperature and dissolved oxygen\nconcentration at the seven lake sites, with the model fitted for all sites together..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,10 +16,6 @@ "oxygen", "Daily", "P1D", - "TOMB", - "TOOK", - "WALK", - "WLOU", "ARIK", "BARC", "BIGC", @@ -49,7 +45,11 @@ "REDB", "SUGG", "SYCA", - "TECR" + "TECR", + "TOMB", + "TOOK", + "WALK", + "WLOU" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/5b824d9568.json b/data/output/neon4cast-stac/60a3c0bc9f.json similarity index 98% rename from data/output/neon4cast-stac/5b824d9568.json rename to data/output/neon4cast-stac/60a3c0bc9f.json index f14336697..7631129f6 100644 --- a/data/output/neon4cast-stac/5b824d9568.json +++ b/data/output/neon4cast-stac/60a3c0bc9f.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:2f2dfcd3a5", "name": "tg_humidity_lm_chla_P1D_summaries summaries", - "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Chlorophyll_a variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: TOOK, BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,6 +16,7 @@ "chla", "Daily", "P1D", + "TOOK", "BARC", "BLWA", "CRAM", @@ -24,8 +25,7 @@ "PRLA", "PRPO", "SUGG", - "TOMB", - "TOOK" + "TOMB" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/b02491dfcc.json b/data/output/neon4cast-stac/628bfe13eb.json similarity index 97% rename from data/output/neon4cast-stac/b02491dfcc.json rename to data/output/neon4cast-stac/628bfe13eb.json index 4562737f6..9381581e1 100644 --- a/data/output/neon4cast-stac/b02491dfcc.json +++ b/data/output/neon4cast-stac/628bfe13eb.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:f4fdd9cdb2", "name": "tg_ets_rcc_90_P1D_scores scores", - "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Red_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,6 +16,28 @@ "rcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", + "KONA", + "KONZ", + "LAJA", + "LENO", + "MLBS", "MOAB", "NIWO", "NOGP", @@ -40,29 +62,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA", - "KONZ", - "LAJA", - "LENO", - "MLBS" + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/7b25db6ebd.json b/data/output/neon4cast-stac/63ffa00b12.json similarity index 97% rename from data/output/neon4cast-stac/7b25db6ebd.json rename to data/output/neon4cast-stac/63ffa00b12.json index 552229913..839bacf8c 100644 --- a/data/output/neon4cast-stac/7b25db6ebd.json +++ b/data/output/neon4cast-stac/63ffa00b12.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:53f3229daf", "name": "tg_tbats_oxygen_P1D_scores scores", - "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,12 +16,6 @@ "oxygen", "Daily", "P1D", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", - "BLWA", "CARI", "COMO", "CRAM", @@ -49,7 +43,13 @@ "TOMB", "TOOK", "WALK", - "WLOU" + "WLOU", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", + "BLWA" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/df58511eed.json b/data/output/neon4cast-stac/65ceceb537.json similarity index 96% rename from data/output/neon4cast-stac/df58511eed.json rename to data/output/neon4cast-stac/65ceceb537.json index ca54afeab..448d34a3d 100644 --- a/data/output/neon4cast-stac/df58511eed.json +++ b/data/output/neon4cast-stac/65ceceb537.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:ba11e250f0", "name": "tg_ets_abundance_P1W_forecast forecasts", - "description": "All forecasts for the Weekly_beetle_community_abundance variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Weekly_beetle_community_abundance variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,20 +16,6 @@ "abundance", "Weekly", "P1W", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", "HEAL", "JERC", "JORN", @@ -62,7 +48,21 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/412330d44b.json b/data/output/neon4cast-stac/66556c5f67.json similarity index 97% rename from data/output/neon4cast-stac/412330d44b.json rename to data/output/neon4cast-stac/66556c5f67.json index f84e5b540..35b36c0f6 100644 --- a/data/output/neon4cast-stac/412330d44b.json +++ b/data/output/neon4cast-stac/66556c5f67.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:92b5fe197b", "name": "persistenceRW_gcc_90_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: DELA, DSNY, GRSM, GUAN, HARV, HEAL, BONA, CLBJ, CPER, DCFS, DEJU, OSBS, PUUM, RMNP, SCBI, SERC, SJER, LAJA, LENO, MLBS, MOAB, NIWO, UNDE, WOOD, WREF, YELL, NOGP, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, ABBY, BARR, BART, BLAN, OAES, ONAQ, ORNL, JERC, JORN, KONA, KONZ.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: DELA, DSNY, GRSM, GUAN, HARV, HEAL, BONA, CLBJ, CPER, DCFS, DEJU, SJER, SOAP, SRER, STEI, STER, TALL, NOGP, OAES, ONAQ, ORNL, OSBS, TEAK, TOOL, TREE, UKFS, UNDE, LAJA, LENO, MLBS, MOAB, NIWO, PUUM, RMNP, SCBI, SERC, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, JERC, JORN, KONA, KONZ.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -27,38 +27,38 @@ "CPER", "DCFS", "DEJU", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", "SJER", - "LAJA", - "LENO", - "MLBS", - "MOAB", - "NIWO", - "UNDE", - "WOOD", - "WREF", - "YELL", - "NOGP", "SOAP", "SRER", "STEI", "STER", "TALL", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", "TEAK", "TOOL", "TREE", "UKFS", + "UNDE", + "LAJA", + "LENO", + "MLBS", + "MOAB", + "NIWO", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", "BLAN", - "OAES", - "ONAQ", - "ORNL", "JERC", "JORN", "KONA", diff --git a/data/output/neon4cast-stac/3ccb131500.json b/data/output/neon4cast-stac/68580d0709.json similarity index 97% rename from data/output/neon4cast-stac/3ccb131500.json rename to data/output/neon4cast-stac/68580d0709.json index 5cf7b82fd..0b6c57a0f 100644 --- a/data/output/neon4cast-stac/3ccb131500.json +++ b/data/output/neon4cast-stac/68580d0709.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:43cd27a92d", "name": "tg_lasso_oxygen_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Dissolved_oxygen variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Dissolved_oxygen variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,6 +16,15 @@ "oxygen", "Daily", "P1D", + "MCRA", + "OKSR", + "POSE", + "PRIN", + "PRLA", + "PRPO", + "REDB", + "SUGG", + "SYCA", "TECR", "TOMB", "TOOK", @@ -40,16 +49,7 @@ "LIRO", "MART", "MAYF", - "MCDI", - "MCRA", - "OKSR", - "POSE", - "PRIN", - "PRLA", - "PRPO", - "REDB", - "SUGG", - "SYCA" + "MCDI" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/54435e490d.json b/data/output/neon4cast-stac/723617b5d9.json similarity index 97% rename from data/output/neon4cast-stac/54435e490d.json rename to data/output/neon4cast-stac/723617b5d9.json index 36be2e893..f46dc40a9 100644 --- a/data/output/neon4cast-stac/54435e490d.json +++ b/data/output/neon4cast-stac/723617b5d9.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:715b9d1e96", "name": "air2waterSat_2_temperature_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Water_temperature variable for the air2waterSat_2 model. Information for the model is provided as follows: The air2water model is a linear model fit using the function lm() in R and uses air temperature as\na covariate.\n The model predicts this variable at the following sites: LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, TOOK, WALK, WLOU.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Water_temperature variable for the air2waterSat_2 model. Information for the model is provided as follows: The air2water model is a linear model fit using the function lm() in R and uses air temperature as\na covariate.\n The model predicts this variable at the following sites: TOOK, WALK, WLOU, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,7 +16,9 @@ "temperature", "Daily", "P1D", - "LEWI", + "TOOK", + "WALK", + "WLOU", "LIRO", "MART", "MAYF", @@ -47,9 +49,7 @@ "HOPB", "KING", "LECO", - "TOOK", - "WALK", - "WLOU" + "LEWI" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/56a4d1e23f.json b/data/output/neon4cast-stac/7b1ea45694.json similarity index 96% rename from data/output/neon4cast-stac/56a4d1e23f.json rename to data/output/neon4cast-stac/7b1ea45694.json index 70a7d3197..1affb700e 100644 --- a/data/output/neon4cast-stac/56a4d1e23f.json +++ b/data/output/neon4cast-stac/7b1ea45694.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:9a24d1e3f9", "name": "cb_prophet_gcc_90_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,6 +16,21 @@ "gcc_90", "Daily", "P1D", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -47,22 +62,7 @@ "OSBS", "PUUM", "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "SCBI" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/848026454e.json b/data/output/neon4cast-stac/7eaaeb9504.json similarity index 99% rename from data/output/neon4cast-stac/848026454e.json rename to data/output/neon4cast-stac/7eaaeb9504.json index 176065160..57011e868 100644 --- a/data/output/neon4cast-stac/848026454e.json +++ b/data/output/neon4cast-stac/7eaaeb9504.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:64e8cd2032", "name": "climatology_rcc_90_P1D_summaries summaries", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, DEJU, HEAL, BONA, TOOL, BARR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, DEJU, HEAL, BONA, BARR, TOOL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -61,8 +61,8 @@ "DEJU", "HEAL", "BONA", - "TOOL", - "BARR" + "BARR", + "TOOL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/f0745f818f.json b/data/output/neon4cast-stac/875165a198.json similarity index 97% rename from data/output/neon4cast-stac/f0745f818f.json rename to data/output/neon4cast-stac/875165a198.json index 6c0ce40e3..826ef8211 100644 --- a/data/output/neon4cast-stac/f0745f818f.json +++ b/data/output/neon4cast-stac/875165a198.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:190e4ee776", "name": "tg_ets_le_P1D_summaries summaries", - "description": "All summaries for the Daily_latent_heat_flux variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_latent_heat_flux variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,29 +16,6 @@ "le", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA", - "KONZ", - "LAJA", - "LENO", - "MLBS", - "MOAB", "NIWO", "NOGP", "OAES", @@ -62,7 +39,30 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", + "KONA", + "KONZ", + "LAJA", + "LENO", + "MLBS", + "MOAB" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/c8b265a2b5.json b/data/output/neon4cast-stac/89ad857c65.json similarity index 97% rename from data/output/neon4cast-stac/c8b265a2b5.json rename to data/output/neon4cast-stac/89ad857c65.json index d115edb74..2aea60ac0 100644 --- a/data/output/neon4cast-stac/c8b265a2b5.json +++ b/data/output/neon4cast-stac/89ad857c65.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:c99c01e7b5", "name": "tg_ets_richness_P1W_scores scores", - "description": "All scores for the Weekly_beetle_community_richness variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Weekly_beetle_community_richness variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,15 +16,6 @@ "richness", "Weekly", "P1W", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -62,7 +53,16 @@ "SOAP", "SRER", "STEI", - "STER" + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/ae85d7402b.json b/data/output/neon4cast-stac/8cf133c119.json similarity index 97% rename from data/output/neon4cast-stac/ae85d7402b.json rename to data/output/neon4cast-stac/8cf133c119.json index e2b32589c..b620d46b6 100644 --- a/data/output/neon4cast-stac/ae85d7402b.json +++ b/data/output/neon4cast-stac/8cf133c119.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:bfefa24d1b", "name": "climatology_temperature_P1D_summaries summaries", - "description": "All summaries for the Daily_Water_temperature variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, COMO, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, SUGG, SYCA, TECR, WALK, WLOU, TOMB, LIRO, PRPO, CRAM, PRLA, CARI, OKSR, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Water_temperature variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, ARIK, BARC, OKSR, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,13 +16,13 @@ "temperature", "Daily", "P1D", - "ARIK", - "BARC", "BIGC", "BLDE", "BLUE", "BLWA", + "CARI", "COMO", + "CRAM", "CUPE", "FLNT", "GUIL", @@ -30,24 +30,24 @@ "KING", "LECO", "LEWI", + "LIRO", "MART", "MAYF", "MCDI", "MCRA", "POSE", "PRIN", + "PRLA", + "PRPO", "REDB", "SUGG", "SYCA", "TECR", + "TOMB", "WALK", "WLOU", - "TOMB", - "LIRO", - "PRPO", - "CRAM", - "PRLA", - "CARI", + "ARIK", + "BARC", "OKSR", "TOOK" ], diff --git a/data/output/neon4cast-stac/0d9d92507c.json b/data/output/neon4cast-stac/8d87364236.json similarity index 96% rename from data/output/neon4cast-stac/0d9d92507c.json rename to data/output/neon4cast-stac/8d87364236.json index b9ec6803c..0d350af51 100644 --- a/data/output/neon4cast-stac/0d9d92507c.json +++ b/data/output/neon4cast-stac/8d87364236.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:296eb005fb", "name": "tg_arima_gcc_90_P1D_scores scores", - "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,20 +16,6 @@ "gcc_90", "Daily", "P1D", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -62,7 +48,21 @@ "PUUM", "RMNP", "SCBI", - "SERC" + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/303a267768.json b/data/output/neon4cast-stac/93dc4acf16.json similarity index 98% rename from data/output/neon4cast-stac/303a267768.json rename to data/output/neon4cast-stac/93dc4acf16.json index c65a0c411..6ebe3b9c2 100644 --- a/data/output/neon4cast-stac/303a267768.json +++ b/data/output/neon4cast-stac/93dc4acf16.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:e5359527f1", "name": "tg_humidity_lm_chla_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Chlorophyll_a variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: TOOK, BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Chlorophyll_a variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,7 +16,6 @@ "chla", "Daily", "P1D", - "TOOK", "BARC", "BLWA", "CRAM", @@ -25,7 +24,8 @@ "PRLA", "PRPO", "SUGG", - "TOMB" + "TOMB", + "TOOK" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/a94dae5182.json b/data/output/neon4cast-stac/9a4ab45a62.json similarity index 97% rename from data/output/neon4cast-stac/a94dae5182.json rename to data/output/neon4cast-stac/9a4ab45a62.json index 8e4f81626..bece454c5 100644 --- a/data/output/neon4cast-stac/a94dae5182.json +++ b/data/output/neon4cast-stac/9a4ab45a62.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:7503922f8a", "name": "tg_arima_temperature_P1D_scores scores", - "description": "All scores for the Daily_Water_temperature variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Water_temperature variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,6 +16,8 @@ "temperature", "Daily", "P1D", + "WALK", + "WLOU", "ARIK", "BARC", "BIGC", @@ -47,9 +49,7 @@ "SYCA", "TECR", "TOMB", - "TOOK", - "WALK", - "WLOU" + "TOOK" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/dfd185dec0.json b/data/output/neon4cast-stac/9bba26e5e4.json similarity index 96% rename from data/output/neon4cast-stac/dfd185dec0.json rename to data/output/neon4cast-stac/9bba26e5e4.json index 6f49987df..bd751d8d6 100644 --- a/data/output/neon4cast-stac/dfd185dec0.json +++ b/data/output/neon4cast-stac/9bba26e5e4.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:2d55c03470", "name": "tg_arima_gcc_90_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,6 +16,10 @@ "gcc_90", "Daily", "P1D", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -58,11 +62,7 @@ "TEAK", "TOOL", "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "UKFS" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/7f919a03c0.json b/data/output/neon4cast-stac/9c08930028.json similarity index 97% rename from data/output/neon4cast-stac/7f919a03c0.json rename to data/output/neon4cast-stac/9c08930028.json index 4ba89cdcd..6df0e3b16 100644 --- a/data/output/neon4cast-stac/7f919a03c0.json +++ b/data/output/neon4cast-stac/9c08930028.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:ab9f669e05", "name": "tg_ets_temperature_P1D_scores scores", - "description": "All scores for the Daily_Water_temperature variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Water_temperature variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,16 +16,6 @@ "temperature", "Daily", "P1D", - "PRLA", - "PRPO", - "REDB", - "SUGG", - "SYCA", - "TECR", - "TOMB", - "TOOK", - "WALK", - "WLOU", "ARIK", "BARC", "BIGC", @@ -49,7 +39,17 @@ "MCRA", "OKSR", "POSE", - "PRIN" + "PRIN", + "PRLA", + "PRPO", + "REDB", + "SUGG", + "SYCA", + "TECR", + "TOMB", + "TOOK", + "WALK", + "WLOU" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/aaf2692345.json b/data/output/neon4cast-stac/a37c290abf.json similarity index 97% rename from data/output/neon4cast-stac/aaf2692345.json rename to data/output/neon4cast-stac/a37c290abf.json index 38e502f7b..64082a864 100644 --- a/data/output/neon4cast-stac/aaf2692345.json +++ b/data/output/neon4cast-stac/a37c290abf.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:f008caa3c3", "name": "tg_humidity_lm_gcc_90_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, DELA, DSNY, GRSM, GUAN, HARV.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Green_chromatic_coordinate variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,6 +16,23 @@ "gcc_90", "Daily", "P1D", + "HEAL", + "JERC", + "JORN", + "KONA", + "KONZ", + "LAJA", + "LENO", + "MLBS", + "MOAB", + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP", "SCBI", "SERC", "SJER", @@ -41,23 +58,6 @@ "CPER", "DCFS", "DEJU", - "HEAL", - "JERC", - "JORN", - "KONA", - "KONZ", - "LAJA", - "LENO", - "MLBS", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", "DELA", "DSNY", "GRSM", diff --git a/data/output/neon4cast-stac/2ae9374936.json b/data/output/neon4cast-stac/a3a156a3d9.json similarity index 97% rename from data/output/neon4cast-stac/2ae9374936.json rename to data/output/neon4cast-stac/a3a156a3d9.json index ae28fb0f3..06f400ae2 100644 --- a/data/output/neon4cast-stac/2ae9374936.json +++ b/data/output/neon4cast-stac/a3a156a3d9.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:231f50d8d2", "name": "tg_humidity_lm_gcc_90_P1D_summaries summaries", - "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,29 +16,6 @@ "gcc_90", "Daily", "P1D", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", - "JORN", - "KONA", - "KONZ", - "LAJA", - "LENO", - "MLBS", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", "SCBI", "SERC", "SJER", @@ -62,7 +39,30 @@ "BONA", "CLBJ", "CPER", - "DCFS" + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", + "JORN", + "KONA", + "KONZ", + "LAJA", + "LENO", + "MLBS", + "MOAB", + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/8365b6ccb5.json b/data/output/neon4cast-stac/a8425a1645.json similarity index 96% rename from data/output/neon4cast-stac/8365b6ccb5.json rename to data/output/neon4cast-stac/a8425a1645.json index af32a0745..3963ed7fa 100644 --- a/data/output/neon4cast-stac/8365b6ccb5.json +++ b/data/output/neon4cast-stac/a8425a1645.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:b94f3d0fe4", "name": "climatology_gcc_90_P1D_scores scores", - "description": "All scores for the Daily_Green_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Green_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,9 +16,6 @@ "gcc_90", "Daily", "P1D", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -62,7 +59,10 @@ "TOOL", "TREE", "UKFS", - "UNDE" + "UNDE", + "WOOD", + "WREF", + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/6ac5ddf693.json b/data/output/neon4cast-stac/aa868376c8.json similarity index 98% rename from data/output/neon4cast-stac/6ac5ddf693.json rename to data/output/neon4cast-stac/aa868376c8.json index 790059ae2..fc9b47653 100644 --- a/data/output/neon4cast-stac/6ac5ddf693.json +++ b/data/output/neon4cast-stac/aa868376c8.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:f8230a8ea5", "name": "tg_tbats_chla_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Chlorophyll_a variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK, BARC.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Chlorophyll_a variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: BARC, BLWA, CRAM, FLNT, LIRO, PRLA, PRPO, SUGG, TOMB, TOOK.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,6 +16,7 @@ "chla", "Daily", "P1D", + "BARC", "BLWA", "CRAM", "FLNT", @@ -24,8 +25,7 @@ "PRPO", "SUGG", "TOMB", - "TOOK", - "BARC" + "TOOK" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/949b63007a.json b/data/output/neon4cast-stac/b4560bc576.json similarity index 97% rename from data/output/neon4cast-stac/949b63007a.json rename to data/output/neon4cast-stac/b4560bc576.json index e618df4a6..db1686b44 100644 --- a/data/output/neon4cast-stac/949b63007a.json +++ b/data/output/neon4cast-stac/b4560bc576.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:11f2dce913", "name": "tg_tbats_temperature_P1D_scores scores", - "description": "All scores for the Daily_Water_temperature variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Water_temperature variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,22 +16,6 @@ "temperature", "Daily", "P1D", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", - "BLWA", - "CARI", - "COMO", - "CRAM", - "CUPE", - "FLNT", - "GUIL", - "HOPB", - "KING", - "LECO", - "LEWI", "LIRO", "MART", "MAYF", @@ -49,7 +33,23 @@ "TOMB", "TOOK", "WALK", - "WLOU" + "WLOU", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", + "BLWA", + "CARI", + "COMO", + "CRAM", + "CUPE", + "FLNT", + "GUIL", + "HOPB", + "KING", + "LECO", + "LEWI" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/7feec4cdae.json b/data/output/neon4cast-stac/c0f4112bd5.json similarity index 96% rename from data/output/neon4cast-stac/7feec4cdae.json rename to data/output/neon4cast-stac/c0f4112bd5.json index 39f2a6755..36f97b0bd 100644 --- a/data/output/neon4cast-stac/7feec4cdae.json +++ b/data/output/neon4cast-stac/c0f4112bd5.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:a12f74d20f", "name": "tg_arima_gcc_90_P1D_summaries summaries", - "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,10 +16,6 @@ "gcc_90", "Daily", "P1D", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -62,7 +58,11 @@ "TEAK", "TOOL", "TREE", - "UKFS" + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/db96551377.json b/data/output/neon4cast-stac/c122e806c8.json similarity index 98% rename from data/output/neon4cast-stac/db96551377.json rename to data/output/neon4cast-stac/c122e806c8.json index 34d5ec608..7cd241a7a 100644 --- a/data/output/neon4cast-stac/db96551377.json +++ b/data/output/neon4cast-stac/c122e806c8.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:23867334a8", "name": "persistenceRW_oxygen_P1D_summaries summaries", - "description": "All summaries for the Daily_Dissolved_oxygen variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: CARI, COMO, CRAM, CUPE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, ARIK, BARC, BIGC, BLDE, BLUE, LECO, LEWI, LIRO, MART, MAYF, TECR, TOMB, TOOK, WALK, BLWA, WLOU, FLNT, GUIL, HOPB, KING, MCDI, MCRA, OKSR, POSE.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Dissolved_oxygen variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: CARI, COMO, CRAM, CUPE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, MAYF, MCDI, MCRA, OKSR, POSE, BLUE, BLWA, WLOU, ARIK, BARC, BIGC, BLDE, TECR, TOMB, TOOK, WALK, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -26,30 +26,30 @@ "REDB", "SUGG", "SYCA", + "MAYF", + "MCDI", + "MCRA", + "OKSR", + "POSE", + "BLUE", + "BLWA", + "WLOU", "ARIK", "BARC", "BIGC", "BLDE", - "BLUE", - "LECO", - "LEWI", - "LIRO", - "MART", - "MAYF", "TECR", "TOMB", "TOOK", "WALK", - "BLWA", - "WLOU", "FLNT", "GUIL", "HOPB", "KING", - "MCDI", - "MCRA", - "OKSR", - "POSE" + "LECO", + "LEWI", + "LIRO", + "MART" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/c769048f25.json b/data/output/neon4cast-stac/c26f3dacb6.json similarity index 97% rename from data/output/neon4cast-stac/c769048f25.json rename to data/output/neon4cast-stac/c26f3dacb6.json index f901c5bc5..d92b57a06 100644 --- a/data/output/neon4cast-stac/c769048f25.json +++ b/data/output/neon4cast-stac/c26f3dacb6.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:9c831fe601", "name": "persistenceRW_oxygen_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Dissolved_oxygen variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: CARI, COMO, CRAM, CUPE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, ARIK, BARC, BIGC, BLDE, BLUE, LECO, LEWI, LIRO, MART, MAYF, TECR, TOMB, TOOK, WALK, BLWA, WLOU, FLNT, GUIL, HOPB, KING, MCDI, MCRA, OKSR, POSE.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Dissolved_oxygen variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: CARI, COMO, CRAM, CUPE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, MAYF, MCDI, MCRA, OKSR, POSE, BLUE, BLWA, WLOU, ARIK, BARC, BIGC, BLDE, TECR, TOMB, TOOK, WALK, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -26,30 +26,30 @@ "REDB", "SUGG", "SYCA", + "MAYF", + "MCDI", + "MCRA", + "OKSR", + "POSE", + "BLUE", + "BLWA", + "WLOU", "ARIK", "BARC", "BIGC", "BLDE", - "BLUE", - "LECO", - "LEWI", - "LIRO", - "MART", - "MAYF", "TECR", "TOMB", "TOOK", "WALK", - "BLWA", - "WLOU", "FLNT", "GUIL", "HOPB", "KING", - "MCDI", - "MCRA", - "OKSR", - "POSE" + "LECO", + "LEWI", + "LIRO", + "MART" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/2154f89767.json b/data/output/neon4cast-stac/c6817ed556.json similarity index 98% rename from data/output/neon4cast-stac/2154f89767.json rename to data/output/neon4cast-stac/c6817ed556.json index 5c2094786..e607c14da 100644 --- a/data/output/neon4cast-stac/2154f89767.json +++ b/data/output/neon4cast-stac/c6817ed556.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:c2c9615de4", "name": "tg_humidity_lm_le_P1D_summaries summaries", - "description": "All summaries for the Daily_latent_heat_flux variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: HARV, HEAL, JERC, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_latent_heat_flux variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,9 +16,6 @@ "le", "Daily", "P1D", - "HARV", - "HEAL", - "JERC", "ABBY", "BARR", "BART", @@ -32,6 +29,9 @@ "DSNY", "GRSM", "GUAN", + "HARV", + "HEAL", + "JERC", "JORN", "KONA", "KONZ", diff --git a/data/output/neon4cast-stac/7a15086bc5.json b/data/output/neon4cast-stac/c8f6b5a4c8.json similarity index 96% rename from data/output/neon4cast-stac/7a15086bc5.json rename to data/output/neon4cast-stac/c8f6b5a4c8.json index 2e590a424..156b193fc 100644 --- a/data/output/neon4cast-stac/7a15086bc5.json +++ b/data/output/neon4cast-stac/c8f6b5a4c8.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:4f7f1f0abb", "name": "climatology_rcc_90_P1D_scores scores", - "description": "All scores for the Daily_Red_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Red_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,29 +16,6 @@ "rcc_90", "Daily", "P1D", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -62,7 +39,30 @@ "LENO", "MLBS", "MOAB", - "NIWO" + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/9633882548.json b/data/output/neon4cast-stac/c961e04ec1.json similarity index 96% rename from data/output/neon4cast-stac/9633882548.json rename to data/output/neon4cast-stac/c961e04ec1.json index 1871681d9..d6a092a7f 100644 --- a/data/output/neon4cast-stac/9633882548.json +++ b/data/output/neon4cast-stac/c961e04ec1.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:678219fc58", "name": "tg_tbats_gcc_90_P1D_summaries summaries", - "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_tbats model. Information for the model is provided as follows: The tg_tbats model is a TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA\nerrors, Trend and Seasonal components) model fit using the function tbats() from the forecast package in\nR (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series model with no\ncovariates..\n The model predicts this variable at the following sites: WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,6 +16,8 @@ "gcc_90", "Daily", "P1D", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -60,9 +62,7 @@ "TREE", "UKFS", "UNDE", - "WOOD", - "WREF", - "YELL" + "WOOD" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/4dcc4d17f8.json b/data/output/neon4cast-stac/cafc6149d2.json similarity index 98% rename from data/output/neon4cast-stac/4dcc4d17f8.json rename to data/output/neon4cast-stac/cafc6149d2.json index 1529a546a..8489b26eb 100644 --- a/data/output/neon4cast-stac/4dcc4d17f8.json +++ b/data/output/neon4cast-stac/cafc6149d2.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:b9d1d8bf4e", "name": "baseline_ensemble_temperature_P1D_summaries summaries", - "description": "All summaries for the Daily_Water_temperature variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: BLWA, COMO, CUPE, FLNT, GUIL, HOPB, SUGG, SYCA, TECR, TOMB, WALK, WLOU, KING, LECO, LEWI, MART, MAYF, MCDI, MCRA, POSE, PRIN, REDB, ARIK, BARC, BIGC, BLDE, BLUE, CRAM, LIRO, PRLA, PRPO, CARI, OKSR, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Water_temperature variable for the baseline_ensemble model. Information for the model is provided as follows: The Baseline MME is a multi-model ensemble (MME) comprised of the two baseline models\n(day-of-year, persistence) submitted by Challenge organisers.\n The model predicts this variable at the following sites: MCDI, MCRA, POSE, PRIN, REDB, SUGG, HOPB, KING, LECO, LEWI, MART, MAYF, SYCA, TECR, TOMB, WALK, WLOU, BLWA, COMO, CUPE, FLNT, GUIL, ARIK, BARC, BIGC, BLDE, BLUE, CRAM, LIRO, PRLA, PRPO, CARI, OKSR, TOOK.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,28 +16,28 @@ "temperature", "Daily", "P1D", - "BLWA", - "COMO", - "CUPE", - "FLNT", - "GUIL", - "HOPB", + "MCDI", + "MCRA", + "POSE", + "PRIN", + "REDB", "SUGG", - "SYCA", - "TECR", - "TOMB", - "WALK", - "WLOU", + "HOPB", "KING", "LECO", "LEWI", "MART", "MAYF", - "MCDI", - "MCRA", - "POSE", - "PRIN", - "REDB", + "SYCA", + "TECR", + "TOMB", + "WALK", + "WLOU", + "BLWA", + "COMO", + "CUPE", + "FLNT", + "GUIL", "ARIK", "BARC", "BIGC", diff --git a/data/output/neon4cast-stac/51d1c41aad.json b/data/output/neon4cast-stac/cd32afbc60.json similarity index 97% rename from data/output/neon4cast-stac/51d1c41aad.json rename to data/output/neon4cast-stac/cd32afbc60.json index 74685d3d4..e6a26ae63 100644 --- a/data/output/neon4cast-stac/51d1c41aad.json +++ b/data/output/neon4cast-stac/cd32afbc60.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:7fbd28cfaa", "name": "tg_ets_gcc_90_P1D_scores scores", - "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Green_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,6 +16,11 @@ "gcc_90", "Daily", "P1D", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -57,12 +62,7 @@ "TALL", "TEAK", "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "TREE" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/c464e4f62d.json b/data/output/neon4cast-stac/cde022ce66.json similarity index 96% rename from data/output/neon4cast-stac/c464e4f62d.json rename to data/output/neon4cast-stac/cde022ce66.json index e0f4f8aab..cecdc23f7 100644 --- a/data/output/neon4cast-stac/c464e4f62d.json +++ b/data/output/neon4cast-stac/cde022ce66.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:85ed7edcc2", "name": "tg_randfor_gcc_90_P1D_summaries summaries", - "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,6 +16,15 @@ "gcc_90", "Daily", "P1D", + "MOAB", + "NIWO", + "NOGP", + "OAES", + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP", "SCBI", "SERC", "SJER", @@ -53,16 +62,7 @@ "KONZ", "LAJA", "LENO", - "MLBS", - "MOAB", - "NIWO", - "NOGP", - "OAES", - "ONAQ", - "ORNL", - "OSBS", - "PUUM", - "RMNP" + "MLBS" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/39aa0b7db1.json b/data/output/neon4cast-stac/ce931c5ca0.json similarity index 97% rename from data/output/neon4cast-stac/39aa0b7db1.json rename to data/output/neon4cast-stac/ce931c5ca0.json index b633b9ae5..8e7cd6271 100644 --- a/data/output/neon4cast-stac/39aa0b7db1.json +++ b/data/output/neon4cast-stac/ce931c5ca0.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:30aedb1974", "name": "cb_prophet_le_P1D_summaries summaries", - "description": "All summaries for the Daily_latent_heat_flux variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: CLBJ, SJER, ONAQ, DSNY, SCBI, MOAB, PUUM, GUAN, OSBS, BART, CPER, HARV, UNDE, STER, KONA, TREE, ABBY, LENO, UKFS, DEJU, KONZ, RMNP, BARR, JORN, SOAP, STEI, TALL, DCFS, TOOL, WOOD, OAES, HEAL, SERC, BLAN, GRSM, ORNL, SRER, NOGP, JERC, DELA, MLBS, NIWO, WREF, LAJA, TEAK, BONA.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_latent_heat_flux variable for the cb_prophet model. Information for the model is provided as follows: The Prophet model is an empirical model, specifically a non-linear regression model that includes\nseasonality effects (Taylor & Letham, 2018). The model relies on Bayesian estimation with an additive\nwhite noise error term.\n The model predicts this variable at the following sites: DSNY, SCBI, MOAB, PUUM, GUAN, BART, CPER, HARV, UNDE, STER, KONA, TREE, ABBY, LENO, UKFS, DEJU, KONZ, RMNP, BARR, JORN, SOAP, STEI, TALL, DCFS, TOOL, WOOD, OAES, HEAL, SERC, BLAN, GRSM, ORNL, SRER, NOGP, JERC, DELA, MLBS, NIWO, WREF, LAJA, TEAK, CLBJ, SJER, OSBS, BONA, ONAQ.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,15 +16,11 @@ "le", "Daily", "P1D", - "CLBJ", - "SJER", - "ONAQ", "DSNY", "SCBI", "MOAB", "PUUM", "GUAN", - "OSBS", "BART", "CPER", "HARV", @@ -61,7 +57,11 @@ "WREF", "LAJA", "TEAK", - "BONA" + "CLBJ", + "SJER", + "OSBS", + "BONA", + "ONAQ" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/d6bca11a77.json b/data/output/neon4cast-stac/d4ee14ee52.json similarity index 97% rename from data/output/neon4cast-stac/d6bca11a77.json rename to data/output/neon4cast-stac/d4ee14ee52.json index 937772494..dfa3fdfe2 100644 --- a/data/output/neon4cast-stac/d6bca11a77.json +++ b/data/output/neon4cast-stac/d4ee14ee52.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:0d3f01f255", "name": "tg_temp_lm_temperature_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Water_temperature variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Water_temperature variable for the tg_temp_lm model. Information for the model is provided as follows: The tg_temp_lm model is a linear model fit using the function lm() in R. This is a very\nsimple model with only one covariate: total precipitation..\n The model predicts this variable at the following sites: TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,6 +16,9 @@ "temperature", "Daily", "P1D", + "TOOK", + "WALK", + "WLOU", "ARIK", "BARC", "BIGC", @@ -46,10 +49,7 @@ "SUGG", "SYCA", "TECR", - "TOMB", - "TOOK", - "WALK", - "WLOU" + "TOMB" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/fd116080e4.json b/data/output/neon4cast-stac/ddb1c4aa17.json similarity index 97% rename from data/output/neon4cast-stac/fd116080e4.json rename to data/output/neon4cast-stac/ddb1c4aa17.json index 70c5cabab..1675171f9 100644 --- a/data/output/neon4cast-stac/fd116080e4.json +++ b/data/output/neon4cast-stac/ddb1c4aa17.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:0a4e5d183b", "name": "tg_randfor_oxygen_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Dissolved_oxygen variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Dissolved_oxygen variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,11 +16,6 @@ "oxygen", "Daily", "P1D", - "TECR", - "TOMB", - "TOOK", - "WALK", - "WLOU", "ARIK", "BARC", "BIGC", @@ -49,7 +44,12 @@ "PRPO", "REDB", "SUGG", - "SYCA" + "SYCA", + "TECR", + "TOMB", + "TOOK", + "WALK", + "WLOU" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/02fc080a6a.json b/data/output/neon4cast-stac/df3646c13b.json similarity index 99% rename from data/output/neon4cast-stac/02fc080a6a.json rename to data/output/neon4cast-stac/df3646c13b.json index d2331f422..5103929aa 100644 --- a/data/output/neon4cast-stac/02fc080a6a.json +++ b/data/output/neon4cast-stac/df3646c13b.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:745c0ba93c", "name": "climatology_gcc_90_P1D_summaries summaries", - "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, BONA, DEJU, HEAL, TOOL, BARR.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Green_chromatic_coordinate variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: ABBY, BART, BLAN, CLBJ, CPER, DCFS, DELA, DSNY, GRSM, GUAN, HARV, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TREE, UKFS, UNDE, WOOD, WREF, YELL, BONA, DEJU, HEAL, BARR, TOOL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -61,8 +61,8 @@ "BONA", "DEJU", "HEAL", - "TOOL", - "BARR" + "BARR", + "TOOL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/1c61a7f9d4.json b/data/output/neon4cast-stac/e21a512e58.json similarity index 97% rename from data/output/neon4cast-stac/1c61a7f9d4.json rename to data/output/neon4cast-stac/e21a512e58.json index 2b04a80dd..17d17c9ca 100644 --- a/data/output/neon4cast-stac/1c61a7f9d4.json +++ b/data/output/neon4cast-stac/e21a512e58.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:f909c21fda", "name": "persistenceRW_nee_P1D_scores scores", - "description": "All scores for the Daily_Net_ecosystem_exchange variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Net_ecosystem_exchange variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,6 +16,9 @@ "nee", "Daily", "P1D", + "WOOD", + "WREF", + "YELL", "ABBY", "BARR", "BART", @@ -59,10 +62,7 @@ "TOOL", "TREE", "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL" + "UNDE" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/bd769a7076.json b/data/output/neon4cast-stac/e323663d39.json similarity index 98% rename from data/output/neon4cast-stac/bd769a7076.json rename to data/output/neon4cast-stac/e323663d39.json index 962fb32b5..eeb8917c9 100644 --- a/data/output/neon4cast-stac/bd769a7076.json +++ b/data/output/neon4cast-stac/e323663d39.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:2576630982", "name": "tg_humidity_lm_le_P1D_forecast forecasts", - "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_latent_heat_flux variable for the tg_humidity_lm model. Information for the model is provided as follows: The tg_humidity_lm model is a linear model fit using the function lm() in R. This is a very simple\nmodel with only one covariate: relative humidity.\n The model predicts this variable at the following sites: HARV, HEAL, JERC, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,6 +16,9 @@ "le", "Daily", "P1D", + "HARV", + "HEAL", + "JERC", "ABBY", "BARR", "BART", @@ -29,9 +32,6 @@ "DSNY", "GRSM", "GUAN", - "HARV", - "HEAL", - "JERC", "JORN", "KONA", "KONZ", diff --git a/data/output/neon4cast-stac/6d7b887a03.json b/data/output/neon4cast-stac/ed90359530.json similarity index 96% rename from data/output/neon4cast-stac/6d7b887a03.json rename to data/output/neon4cast-stac/ed90359530.json index fda0c465f..955f26c9f 100644 --- a/data/output/neon4cast-stac/6d7b887a03.json +++ b/data/output/neon4cast-stac/ed90359530.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:e1d19fb287", "name": "tg_randfor_rcc_90_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_randfor model. Information for the model is provided as follows: Random Forest is a machine learning model that is fitted with the ranger() function in the ranger\nR package (Wright & Ziegler 2017) within the tidymodels framework (Kuhn & Wickham 2020). The\nmodel drivers are unlagged air temperature, air pressure, relative humidity, surface downwelling\nlongwave and shortwave radiation, precipitation, and northward and eastward wind.\n The model predicts this variable at the following sites: JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,22 +16,6 @@ "rcc_90", "Daily", "P1D", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC", "JORN", "KONA", "KONZ", @@ -62,7 +46,23 @@ "UNDE", "WOOD", "WREF", - "YELL" + "YELL", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/c4629f9667.json b/data/output/neon4cast-stac/ef06ecc0d2.json similarity index 97% rename from data/output/neon4cast-stac/c4629f9667.json rename to data/output/neon4cast-stac/ef06ecc0d2.json index 56904cc8f..e187ffc53 100644 --- a/data/output/neon4cast-stac/c4629f9667.json +++ b/data/output/neon4cast-stac/ef06ecc0d2.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:009f6fb0c1", "name": "fARIMA_clim_ensemble_temperature_P1D_scores scores", - "description": "All scores for the Daily_Water_temperature variable for the fARIMA_clim_ensemble model. Information for the model is provided as follows: The fAMIRA-DOY MME is a multi-model ensemble (MME) composed of two empirical\nmodels: an ARIMA model (fARIMA) and day-of-year model.\n The model predicts this variable at the following sites: GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, SUGG, SYCA, TECR, TOMB, WALK, WLOU, ARIK, BARC, BLDE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, REDB, BIGC, TOOK.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Water_temperature variable for the fARIMA_clim_ensemble model. Information for the model is provided as follows: The fAMIRA-DOY MME is a multi-model ensemble (MME) composed of two empirical\nmodels: an ARIMA model (fARIMA) and day-of-year model.\n The model predicts this variable at the following sites: BLDE, CARI, COMO, CRAM, CUPE, FLNT, GUIL, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TOMB, WALK, WLOU, ARIK, BARC, BIGC, HOPB, TOOK, TECR, BLWA.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,8 +16,13 @@ "temperature", "Daily", "P1D", + "BLDE", + "CARI", + "COMO", + "CRAM", + "CUPE", + "FLNT", "GUIL", - "HOPB", "KING", "LECO", "LEWI", @@ -31,24 +36,19 @@ "PRIN", "PRLA", "PRPO", + "REDB", "SUGG", "SYCA", - "TECR", "TOMB", "WALK", "WLOU", "ARIK", "BARC", - "BLDE", - "BLWA", - "CARI", - "COMO", - "CRAM", - "CUPE", - "FLNT", - "REDB", "BIGC", - "TOOK" + "HOPB", + "TOOK", + "TECR", + "BLWA" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/2274041530.json b/data/output/neon4cast-stac/f2b2ceebd7.json similarity index 97% rename from data/output/neon4cast-stac/2274041530.json rename to data/output/neon4cast-stac/f2b2ceebd7.json index c1746ed5f..2a92ca1ab 100644 --- a/data/output/neon4cast-stac/2274041530.json +++ b/data/output/neon4cast-stac/f2b2ceebd7.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:9e56bc1104", "name": "tg_arima_oxygen_P1D_scores scores", - "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Dissolved_oxygen variable for the tg_arima model. Information for the model is provided as follows: The tg_arima model is an AutoRegressive Integrated Moving Average (ARIMA) model fit using\nthe function auto.arima() from the forecast package in R (Hyndman et al. 2023; Hyndman et al., 2008).\nThis is an empirical time series model with no covariates.\n The model predicts this variable at the following sites: POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,6 +16,18 @@ "oxygen", "Daily", "P1D", + "POSE", + "PRIN", + "PRLA", + "PRPO", + "REDB", + "SUGG", + "SYCA", + "TECR", + "TOMB", + "TOOK", + "WALK", + "WLOU", "ARIK", "BARC", "BIGC", @@ -37,19 +49,7 @@ "MAYF", "MCDI", "MCRA", - "OKSR", - "POSE", - "PRIN", - "PRLA", - "PRPO", - "REDB", - "SUGG", - "SYCA", - "TECR", - "TOMB", - "TOOK", - "WALK", - "WLOU" + "OKSR" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/83ee1e48fa.json b/data/output/neon4cast-stac/f4ca151278.json similarity index 96% rename from data/output/neon4cast-stac/83ee1e48fa.json rename to data/output/neon4cast-stac/f4ca151278.json index c28d3ff47..d1fa4cc20 100644 --- a/data/output/neon4cast-stac/83ee1e48fa.json +++ b/data/output/neon4cast-stac/f4ca151278.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:09955825b1", "name": "tg_lasso_rcc_90_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_lasso model. Information for the model is provided as follows: Lasso is a machine learning model implemented in the same workflow as tg_randfor, but with\ndifferent hyperparameter tuning. The model drivers are unlagged air temperature, air pressure, relative\nhumidity, surface downwelling longwave and shortwave radiation, precipitation, and northward and\neastward wind. Lasso regressions were fitted with the function glmnet() in\nthe package glmnet (Tay et al. 2023), where the regularization hyperparameter (lambda) is tuned and\nselected with 10-fold cross validation..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,6 +16,22 @@ "rcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "BART", + "BLAN", + "BONA", + "CLBJ", + "CPER", + "DCFS", + "DEJU", + "DELA", + "DSNY", + "GRSM", + "GUAN", + "HARV", + "HEAL", + "JERC", "JORN", "KONA", "KONZ", @@ -46,23 +62,7 @@ "UNDE", "WOOD", "WREF", - "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", - "CPER", - "DCFS", - "DEJU", - "DELA", - "DSNY", - "GRSM", - "GUAN", - "HARV", - "HEAL", - "JERC" + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/4111cbe19a.json b/data/output/neon4cast-stac/f754d01f1a.json similarity index 97% rename from data/output/neon4cast-stac/4111cbe19a.json rename to data/output/neon4cast-stac/f754d01f1a.json index c14a5d289..4bf2beafe 100644 --- a/data/output/neon4cast-stac/4111cbe19a.json +++ b/data/output/neon4cast-stac/f754d01f1a.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:e1a2f8b342", "name": "persistenceRW_rcc_90_P1D_summaries summaries", - "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: KONA, KONZ, LAJA, LENO, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, ONAQ, ORNL, OSBS, PUUM, SRER, STEI, STER, TALL, TEAK, RMNP, SCBI, SERC, SJER, SOAP, OAES, JERC, JORN, MLBS, MOAB, NIWO, NOGP, TOOL, TREE, UKFS, UNDE, GRSM, GUAN, HARV, HEAL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", + "description": "All summaries for the Daily_Red_chromatic_coordinate variable for the persistenceRW model. Information for the model is provided as follows: Random walk from the fable package with ensembles used to represent uncertainty.\n The model predicts this variable at the following sites: ABBY, BARR, RMNP, SCBI, SERC, SJER, SOAP, JERC, JORN, KONA, KONZ, LAJA, LENO, WOOD, WREF, YELL, SRER, STEI, STER, TALL, TEAK, CPER, DCFS, DEJU, DELA, DSNY, OAES, ONAQ, ORNL, OSBS, PUUM, MLBS, MOAB, NIWO, NOGP, TOOL, TREE, UKFS, UNDE, BART, BLAN, BONA, CLBJ, GRSM, GUAN, HARV, HEAL.\n Summaries are the forecasts statistics of the raw forecasts (i.e., mean, median, confidence intervals)", "datePublished": "2022-01-01", "keywords": [ "Summaries", @@ -16,6 +16,15 @@ "rcc_90", "Daily", "P1D", + "ABBY", + "BARR", + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "JERC", + "JORN", "KONA", "KONZ", "LAJA", @@ -23,34 +32,21 @@ "WOOD", "WREF", "YELL", - "ABBY", - "BARR", - "BART", - "BLAN", - "BONA", - "CLBJ", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", "CPER", "DCFS", "DEJU", "DELA", "DSNY", + "OAES", "ONAQ", "ORNL", "OSBS", "PUUM", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "OAES", - "JERC", - "JORN", "MLBS", "MOAB", "NIWO", @@ -59,6 +55,10 @@ "TREE", "UKFS", "UNDE", + "BART", + "BLAN", + "BONA", + "CLBJ", "GRSM", "GUAN", "HARV", diff --git a/data/output/neon4cast-stac/0c83e1818f.json b/data/output/neon4cast-stac/f87e6e2260.json similarity index 96% rename from data/output/neon4cast-stac/0c83e1818f.json rename to data/output/neon4cast-stac/f87e6e2260.json index ed1934955..ffb41c92a 100644 --- a/data/output/neon4cast-stac/0c83e1818f.json +++ b/data/output/neon4cast-stac/f87e6e2260.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:12ef1ad854", "name": "tg_ets_rcc_90_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL, ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Red_chromatic_coordinate variable for the tg_ets model. Information for the model is provided as follows: The tg_ets model is an Error, Trend, Seasonal (ETS) model fit using the function ets() from the\nforecast package in R (Hyndman et al. 2023; Hyndman et al., 2008). This is an empirical time series\nmodel with no covariates..\n The model predicts this variable at the following sites: ABBY, BARR, BART, BLAN, BONA, CLBJ, CPER, DCFS, DEJU, DELA, DSNY, GRSM, GUAN, HARV, HEAL, JERC, JORN, KONA, KONZ, LAJA, LENO, MLBS, MOAB, NIWO, NOGP, OAES, ONAQ, ORNL, OSBS, PUUM, RMNP, SCBI, SERC, SJER, SOAP, SRER, STEI, STER, TALL, TEAK, TOOL, TREE, UKFS, UNDE, WOOD, WREF, YELL.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,26 +16,6 @@ "rcc_90", "Daily", "P1D", - "ORNL", - "OSBS", - "PUUM", - "RMNP", - "SCBI", - "SERC", - "SJER", - "SOAP", - "SRER", - "STEI", - "STER", - "TALL", - "TEAK", - "TOOL", - "TREE", - "UKFS", - "UNDE", - "WOOD", - "WREF", - "YELL", "ABBY", "BARR", "BART", @@ -62,7 +42,27 @@ "NIWO", "NOGP", "OAES", - "ONAQ" + "ONAQ", + "ORNL", + "OSBS", + "PUUM", + "RMNP", + "SCBI", + "SERC", + "SJER", + "SOAP", + "SRER", + "STEI", + "STER", + "TALL", + "TEAK", + "TOOL", + "TREE", + "UKFS", + "UNDE", + "WOOD", + "WREF", + "YELL" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/neon4cast-stac/2ec1a1b38d.json b/data/output/neon4cast-stac/fec91bb8f9.json similarity index 97% rename from data/output/neon4cast-stac/2ec1a1b38d.json rename to data/output/neon4cast-stac/fec91bb8f9.json index 1ab86952f..fe8df415d 100644 --- a/data/output/neon4cast-stac/2ec1a1b38d.json +++ b/data/output/neon4cast-stac/fec91bb8f9.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:neon4cast-stac:e46a01488a", "name": "fTSLM_lag_temperature_P1D_scores scores", - "description": "All scores for the Daily_Water_temperature variable for the fTSLM_lag model. Information for the model is provided as follows: This is a simple time series linear model in which water temperature is a function of air\ntemperature of that day and the previous day\u2019s air temperature.\n The model predicts this variable at the following sites: CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU, ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Water_temperature variable for the fTSLM_lag model. Information for the model is provided as follows: This is a simple time series linear model in which water temperature is a function of air\ntemperature of that day and the previous day\u2019s air temperature.\n The model predicts this variable at the following sites: ARIK, BARC, BIGC, BLDE, BLUE, BLWA, CARI, COMO, CRAM, CUPE, FLNT, GUIL, HOPB, KING, LECO, LEWI, LIRO, MART, MAYF, MCDI, MCRA, OKSR, POSE, PRIN, PRLA, PRPO, REDB, SUGG, SYCA, TECR, TOMB, TOOK, WALK, WLOU.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,6 +16,15 @@ "temperature", "Daily", "P1D", + "ARIK", + "BARC", + "BIGC", + "BLDE", + "BLUE", + "BLWA", + "CARI", + "COMO", + "CRAM", "CUPE", "FLNT", "GUIL", @@ -40,16 +49,7 @@ "TOMB", "TOOK", "WALK", - "WLOU", - "ARIK", - "BARC", - "BIGC", - "BLDE", - "BLUE", - "BLWA", - "CARI", - "COMO", - "CRAM" + "WLOU" ], "citation": { "@type": "CreativeWork", diff --git a/data/output/sitemap/sitemap_neon4cast.xml b/data/output/sitemap/sitemap_neon4cast.xml index 80dcef4ad..b9ecb041c 100644 --- a/data/output/sitemap/sitemap_neon4cast.xml +++ b/data/output/sitemap/sitemap_neon4cast.xml @@ -1,9 +1,7 @@ https://raw.githubusercontent.com/earthcube/stacIndexer/master/data/output/neon4cast-stac/31d91c5392.json - https://raw.githubusercontent.com/earthcube/stacIndexer/master/data/output/neon4cast-stac/5d46bfea09.json https://raw.githubusercontent.com/earthcube/stacIndexer/master/data/output/neon4cast-stac/2dad7629c7.json - 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"description": "All forecasts for the Daily_oxygen_concentration variable for the fableNNETAR_focal model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_oxygen_concentration variable for the fableNNETAR_focal model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,8 +16,8 @@ "DO_mgL_mean", "Daily", "P1D", - "fcre", "bvre", + "fcre", "machine learning" ], "citation": { diff --git a/data/output/vera4cast-stac/06139c6541.json b/data/output/vera4cast-stac/06139c6541.json new file mode 100644 index 000000000..cec017170 --- /dev/null +++ b/data/output/vera4cast-stac/06139c6541.json @@ -0,0 +1,266 @@ +{ + "@context": { + "@vocab": "https://schema.org/" + }, + "@type": "Dataset", + "@id": "urn:vera4cast-stac:44c26a3be9", + "name": "asl.climate.window_DO_mgL_mean_P1D_scores scores", + "description": "All scores for the Daily_oxygen_concentration variable for the asl.climate.window model. Information for the model is provided as follows: Climatology, but with a rolling 10-day window for historical data. This works really well for my methane forecasts at SERC, so I thought it might be useful here.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datePublished": "2022-01-01", + "keywords": [ + "Scores", + "vera4cast", + "Chemical", + "asl.climate.window", + "oxygen_concentration", + "DO_mgL_mean", + "Daily", + "P1D", + "bvre", + "fcre", + "empirical" + ], + "citation": { + "@type": "CreativeWork", + "@id": "https://doi.org/10.1002/ecs2.4686", + "url": "https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecs2.4686", + "name": "A community convention for ecological forecasting: Output files and metadata version 1.0", + "description": "This paper summarizes the open community conventions developed by the Ecological Forecasting Initiative (EFI) for the common formatting and archiving of ecological forecasts and the metadata associated with these forecasts. ", + "identifier": { + "@type": "PropertyValue", + "propertyID": "https://registry.identifiers.org/registry/doi", + "value": "doi:10.1002/ecs2.4686", + "url": "https://doi.org/10.1002/ecs2.4686" + } + }, + "variableMeasured": [ + { + "@type": "PropertyValue", + "name": "reference_datetime", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "@type": "PropertyValue", + "name": "site_id", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." + }, + { + "@type": "PropertyValue", + "name": "datetime", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "@type": "PropertyValue", + "name": "family", + "description": "For ensembles: \u201censemble.\u201d Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The \u201csample\u201d distribution is synonymous with \u201censemble.\u201d For summary statistics: \u201csummary.\u201dIf this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." + }, + { + "@type": "PropertyValue", + "name": "pub_datetime", + "description": "datetime that forecast was submitted" + }, + { + "@type": "PropertyValue", + "name": "depth_m", + "description": "depth (meters) in water column of prediction" + }, + { + "@type": "PropertyValue", + "name": "observation", + "description": "observed value for variable" + }, + { + "@type": "PropertyValue", + "name": "crps", + "description": "crps forecast score" + }, + { + "@type": "PropertyValue", + "name": "logs", + "description": "logs forecast score" + }, + { + "@type": "PropertyValue", + "name": "mean", + "description": "mean forecast prediction" + }, + { + "@type": "PropertyValue", + "name": "median", + "description": "median forecast prediction" + }, + { + "@type": "PropertyValue", + "name": "sd", + "description": "standard deviation forecasts" + }, + { + "@type": "PropertyValue", + "name": "quantile97.5", + "description": "upper 97.5 percentile value of forecast" + }, + { + "@type": "PropertyValue", + "name": "quantile02.5", + "description": "upper 2.5 percentile value of forecast" + }, + { + "@type": "PropertyValue", + "name": "quantile90", + "description": "upper 90 percentile value of forecast" + }, + { + "@type": "PropertyValue", + "name": "quantile10", + "description": "upper 10 percentile value of forecast" + }, + { + "@type": "PropertyValue", + "name": "duration", + "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" + }, + { + "@type": "PropertyValue", + "name": "model_id", + "description": "unique model identifier" + }, + { + "@type": "PropertyValue", + "name": "project_id", + "description": "unique project identifier" + }, + { + "@type": "PropertyValue", + "name": "variable", + "description": "name of forecasted variable" + } + ], + "distribution": [ + { + "@type": "DataDownload", + "contentUrl": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.climate.window.json", + "encodingFormat": "application/json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.climate.window.json\")\n\n", + "name": "Model Metadata" + }, + { + "@type": "DataDownload", + "contentUrl": "https://github.com/abbylewis/vera_meteor_strike", + "encodingFormat": "text/html", + "description": "The link to the model code provided by the model submission team", + "name": "Link for Model Code" + }, + { + "@type": "DataDownload", + "contentUrl": "s3://anonymous@bio230121-bucket01/vera4cast/scores/bundled-parquetproject_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.climate.window?endpoint_override=renc.osn.xsede.org", + "encodingFormat": "application/x-parquet", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/bundled-parquetproject_id=vera4cast/duration=P1D/variable=DO_mgL_mean/model_id=asl.climate.window?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n", + "name": "Database Access for Daily oxygen_concentration" + } + ], + "spatialCoverage": { + "@type": "Place", + "geo": { + "@type": "GeoShape", + "box": "-79.8372 37.3032 -79.8159 37.3129" + }, + "additionalProperty": [ + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291041754864156672 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291043953887412224 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291044503643226112 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291045878032760832 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291047252422295552 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291047802178109440 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291051650468806656 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291052200224620544 + } + ] + } +} \ No newline at end of file diff --git a/data/output/vera4cast-stac/ede1dc40de.json b/data/output/vera4cast-stac/074cf2d9b8.json similarity index 99% rename from data/output/vera4cast-stac/ede1dc40de.json rename to data/output/vera4cast-stac/074cf2d9b8.json index 18ee0d850..914c0fdde 100644 --- a/data/output/vera4cast-stac/ede1dc40de.json +++ b/data/output/vera4cast-stac/074cf2d9b8.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:vera4cast-stac:559c036e28", "name": "fableNNETAR_DO_mgL_mean_P1D_forecast forecasts", - "description": "All forecasts for the Daily_oxygen_concentration variable for the fableNNETAR model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_oxygen_concentration variable for the fableNNETAR model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,8 +16,8 @@ "DO_mgL_mean", "Daily", "P1D", - "bvre", "fcre", + "bvre", "machine learning" ], "citation": { diff --git a/data/output/vera4cast-stac/91e53342fa.json b/data/output/vera4cast-stac/16a8522736.json similarity index 100% rename from data/output/vera4cast-stac/91e53342fa.json rename to data/output/vera4cast-stac/16a8522736.json index 086e95100..47116326a 100644 --- a/data/output/vera4cast-stac/91e53342fa.json +++ b/data/output/vera4cast-stac/16a8522736.json @@ -16,8 +16,8 @@ "Chla_ugL_mean", "Daily", "P1D", - "bvre", "fcre", + "bvre", "empirical" ], "citation": { diff --git a/data/output/vera4cast-stac/7a7fa581f7.json b/data/output/vera4cast-stac/2767aff0d4.json similarity index 100% rename from data/output/vera4cast-stac/7a7fa581f7.json rename to data/output/vera4cast-stac/2767aff0d4.json index 286b97d83..8a675e992 100644 --- a/data/output/vera4cast-stac/7a7fa581f7.json +++ b/data/output/vera4cast-stac/2767aff0d4.json @@ -16,8 +16,8 @@ "Chla_ugL_mean", "Daily", "P1D", - "fcre", "bvre", + "fcre", "machine learning" ], "citation": { diff --git a/data/output/vera4cast-stac/540a805da3.json b/data/output/vera4cast-stac/4fbb491c41.json similarity index 100% rename from data/output/vera4cast-stac/540a805da3.json rename to data/output/vera4cast-stac/4fbb491c41.json index cf4dd015c..883e1b01a 100644 --- a/data/output/vera4cast-stac/540a805da3.json +++ b/data/output/vera4cast-stac/4fbb491c41.json @@ -16,8 +16,8 @@ "DO_mgL_mean", "Daily", "P1D", - "fcre", "bvre", + "fcre", "machine learning" ], "citation": { diff --git a/data/output/vera4cast-stac/19e005c7ab.json b/data/output/vera4cast-stac/5d8e0b5934.json similarity index 99% rename from data/output/vera4cast-stac/19e005c7ab.json rename to data/output/vera4cast-stac/5d8e0b5934.json index f7c3880ef..75e9c0d34 100644 --- a/data/output/vera4cast-stac/19e005c7ab.json +++ b/data/output/vera4cast-stac/5d8e0b5934.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:vera4cast-stac:851ebc8711", "name": "asl.tbats_Chla_ugL_mean_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Chlorophyll-a variable for the asl.tbats model. Information for the model is provided as follows: forecast::tbats() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Chlorophyll-a variable for the asl.tbats model. Information for the model is provided as follows: forecast::tbats() function in R, fit individually at each site/depth.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,8 +16,8 @@ "Chla_ugL_mean", "Daily", "P1D", - "bvre", "fcre", + "bvre", "empirical" ], "citation": { diff --git a/data/output/vera4cast-stac/5f3f524e6a.json b/data/output/vera4cast-stac/5f3f524e6a.json new file mode 100644 index 000000000..7fbd21379 --- /dev/null +++ b/data/output/vera4cast-stac/5f3f524e6a.json @@ -0,0 +1,266 @@ +{ + "@context": { + "@vocab": "https://schema.org/" + }, + "@type": "Dataset", + "@id": "urn:vera4cast-stac:2940f5b274", + "name": "asl.climate.window_Chla_ugL_mean_P1D_scores scores", + "description": "All scores for the Daily_Chlorophyll-a variable for the asl.climate.window model. Information for the model is provided as follows: Climatology, but with a rolling 10-day window for historical data. This works really well for my methane forecasts at SERC, so I thought it might be useful here.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datePublished": "2022-01-01", + "keywords": [ + "Scores", + "vera4cast", + "Biological", + "asl.climate.window", + "Chlorophyll-a", + "Chla_ugL_mean", + "Daily", + "P1D", + "bvre", + "fcre", + "empirical" + ], + "citation": { + "@type": "CreativeWork", + "@id": "https://doi.org/10.1002/ecs2.4686", + "url": "https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecs2.4686", + "name": "A community convention for ecological forecasting: Output files and metadata version 1.0", + "description": "This paper summarizes the open community conventions developed by the Ecological Forecasting Initiative (EFI) for the common formatting and archiving of ecological forecasts and the metadata associated with these forecasts. ", + "identifier": { + "@type": "PropertyValue", + "propertyID": "https://registry.identifiers.org/registry/doi", + "value": "doi:10.1002/ecs2.4686", + "url": "https://doi.org/10.1002/ecs2.4686" + } + }, + "variableMeasured": [ + { + "@type": "PropertyValue", + "name": "reference_datetime", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "@type": "PropertyValue", + "name": "site_id", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." + }, + { + "@type": "PropertyValue", + "name": "datetime", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "@type": "PropertyValue", + "name": "family", + "description": "For ensembles: \u201censemble.\u201d Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The \u201csample\u201d distribution is synonymous with \u201censemble.\u201d For summary statistics: \u201csummary.\u201dIf this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." + }, + { + "@type": "PropertyValue", + "name": "pub_datetime", + "description": "datetime that forecast was submitted" + }, + { + "@type": "PropertyValue", + "name": "depth_m", + "description": "depth (meters) in water column of prediction" + }, + { + "@type": "PropertyValue", + "name": "observation", + "description": "observed value for variable" + }, + { + "@type": "PropertyValue", + "name": "crps", + "description": "crps forecast score" + }, + { + "@type": "PropertyValue", + "name": "logs", + "description": "logs forecast score" + }, + { + "@type": "PropertyValue", + "name": "mean", + "description": "mean forecast prediction" + }, + { + "@type": "PropertyValue", + "name": "median", + "description": "median forecast prediction" + }, + { + "@type": "PropertyValue", + "name": "sd", + "description": "standard deviation forecasts" + }, + { + "@type": "PropertyValue", + "name": "quantile97.5", + "description": "upper 97.5 percentile value of forecast" + }, + { + "@type": "PropertyValue", + "name": "quantile02.5", + "description": "upper 2.5 percentile value of forecast" + }, + { + "@type": "PropertyValue", + "name": "quantile90", + "description": "upper 90 percentile value of forecast" + }, + { + "@type": "PropertyValue", + "name": "quantile10", + "description": "upper 10 percentile value of forecast" + }, + { + "@type": "PropertyValue", + "name": "duration", + "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" + }, + { + "@type": "PropertyValue", + "name": "model_id", + "description": "unique model identifier" + }, + { + "@type": "PropertyValue", + "name": "project_id", + "description": "unique project identifier" + }, + { + "@type": "PropertyValue", + "name": "variable", + "description": "name of forecasted variable" + } + ], + "distribution": [ + { + "@type": "DataDownload", + "contentUrl": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.climate.window.json", + "encodingFormat": "application/json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.climate.window.json\")\n\n", + "name": "Model Metadata" + }, + { + "@type": "DataDownload", + "contentUrl": "https://github.com/abbylewis/vera_meteor_strike", + "encodingFormat": "text/html", + "description": "The link to the model code provided by the model submission team", + "name": "Link for Model Code" + }, + { + "@type": "DataDownload", + "contentUrl": "s3://anonymous@bio230121-bucket01/vera4cast/scores/bundled-parquetproject_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=asl.climate.window?endpoint_override=renc.osn.xsede.org", + "encodingFormat": "application/x-parquet", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/bundled-parquetproject_id=vera4cast/duration=P1D/variable=Chla_ugL_mean/model_id=asl.climate.window?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n", + "name": "Database Access for Daily Chlorophyll-a" + } + ], + "spatialCoverage": { + "@type": "Place", + "geo": { + "@type": "GeoShape", + "box": "-79.8372 37.3032 -79.8159 37.3129" + }, + "additionalProperty": [ + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291041754864156672 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291043953887412224 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291044503643226112 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291045878032760832 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291047252422295552 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291047802178109440 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291051650468806656 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291052200224620544 + } + ] + } +} \ No newline at end of file diff --git a/data/output/vera4cast-stac/0c55255bd2.json b/data/output/vera4cast-stac/6351bd3178.json similarity index 99% rename from data/output/vera4cast-stac/0c55255bd2.json rename to data/output/vera4cast-stac/6351bd3178.json index 1e3eb30fc..dec7cd112 100644 --- a/data/output/vera4cast-stac/0c55255bd2.json +++ b/data/output/vera4cast-stac/6351bd3178.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:vera4cast-stac:a30c8f8698", "name": "fableNNETAR_Temp_C_mean_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Water_temperature variable for the fableNNETAR model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Water_temperature variable for the fableNNETAR model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,8 +16,8 @@ "Temp_C_mean", "Daily", "P1D", - "fcre", "bvre", + "fcre", "machine learning" ], "citation": { diff --git a/data/output/vera4cast-stac/b1fc98b45b.json b/data/output/vera4cast-stac/7987bf6594.json similarity index 100% rename from data/output/vera4cast-stac/b1fc98b45b.json rename to data/output/vera4cast-stac/7987bf6594.json index 0e4111974..1bb126600 100644 --- a/data/output/vera4cast-stac/b1fc98b45b.json +++ b/data/output/vera4cast-stac/7987bf6594.json @@ -16,9 +16,9 @@ "Temp_C_mean", "Daily", "P1D", + "tubr", "bvre", "fcre", - "tubr", "empirical" ], "citation": { diff --git a/data/output/vera4cast-stac/c7e275d5a5.json b/data/output/vera4cast-stac/8163ce2928.json similarity index 99% rename from data/output/vera4cast-stac/c7e275d5a5.json rename to data/output/vera4cast-stac/8163ce2928.json index 508c34504..edf6c217f 100644 --- a/data/output/vera4cast-stac/c7e275d5a5.json +++ b/data/output/vera4cast-stac/8163ce2928.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:vera4cast-stac:f4256a1ce2", "name": "climatology_Secchi_m_sample_P1D_scores scores", - "description": "All scores for the Daily_Secchi variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Secchi variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: fcre, bvre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,8 +16,8 @@ "Secchi_m_sample", "Daily", "P1D", - "bvre", "fcre", + "bvre", "empirical" ], "citation": { diff --git a/data/output/vera4cast-stac/38bb8d3d0d.json b/data/output/vera4cast-stac/a688e965a2.json similarity index 99% rename from data/output/vera4cast-stac/38bb8d3d0d.json rename to data/output/vera4cast-stac/a688e965a2.json index 0c92515b8..0983f9630 100644 --- a/data/output/vera4cast-stac/38bb8d3d0d.json +++ b/data/output/vera4cast-stac/a688e965a2.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:vera4cast-stac:cdd3cdd1e2", "name": "fableNNETAR_Bloom_binary_mean_P1D_scores scores", - "description": "All scores for the Daily_Bloom_binary variable for the fableNNETAR model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package.\n The model predicts this variable at the following sites: fcre, bvre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Bloom_binary variable for the fableNNETAR model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,8 +16,8 @@ "Bloom_binary_mean", "Daily", "P1D", - "fcre", "bvre", + "fcre", "machine learning" ], "citation": { diff --git a/data/output/vera4cast-stac/db8daf1792.json b/data/output/vera4cast-stac/ad9711ad97.json similarity index 99% rename from data/output/vera4cast-stac/db8daf1792.json rename to data/output/vera4cast-stac/ad9711ad97.json index c67aba21a..a359377e3 100644 --- a/data/output/vera4cast-stac/db8daf1792.json +++ b/data/output/vera4cast-stac/ad9711ad97.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:vera4cast-stac:be3e25c3bd", "name": "fableNNETAR_Chla_ugL_mean_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Chlorophyll-a variable for the fableNNETAR model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Chlorophyll-a variable for the fableNNETAR model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,8 +16,8 @@ "Chla_ugL_mean", "Daily", "P1D", - "fcre", "bvre", + "fcre", "machine learning" ], "citation": { diff --git a/data/output/vera4cast-stac/b46f13d948.json b/data/output/vera4cast-stac/c647c387fd.json similarity index 99% rename from data/output/vera4cast-stac/b46f13d948.json rename to data/output/vera4cast-stac/c647c387fd.json index 4e8ab1c3e..c3cf448d7 100644 --- a/data/output/vera4cast-stac/b46f13d948.json +++ b/data/output/vera4cast-stac/c647c387fd.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:vera4cast-stac:0adf0fcd72", "name": "climatology_Temp_C_mean_P1D_scores scores", - "description": "All scores for the Daily_Water_temperature variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: tubr, bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "description": "All scores for the Daily_Water_temperature variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: bvre, fcre, tubr.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", "datePublished": "2022-01-01", "keywords": [ "Scores", @@ -16,9 +16,9 @@ "Temp_C_mean", "Daily", "P1D", - "tubr", "bvre", "fcre", + "tubr", "empirical" ], "citation": { diff --git a/data/output/vera4cast-stac/4fba5724a5.json b/data/output/vera4cast-stac/ccdfff3927.json similarity index 100% rename from data/output/vera4cast-stac/4fba5724a5.json rename to data/output/vera4cast-stac/ccdfff3927.json index bb11f9c58..b1d254844 100644 --- a/data/output/vera4cast-stac/4fba5724a5.json +++ b/data/output/vera4cast-stac/ccdfff3927.json @@ -16,8 +16,8 @@ "Temp_C_mean", "Daily", "P1D", - "fcre", "bvre", + "fcre", "machine learning" ], "citation": { diff --git a/data/output/vera4cast-stac/573c158ece.json b/data/output/vera4cast-stac/d045f62c2c.json similarity index 99% rename from data/output/vera4cast-stac/573c158ece.json rename to data/output/vera4cast-stac/d045f62c2c.json index fce628517..591767d8b 100644 --- a/data/output/vera4cast-stac/573c158ece.json +++ b/data/output/vera4cast-stac/d045f62c2c.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:vera4cast-stac:36e13f6944", "name": "fableNNETAR_focal_Secchi_m_sample_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Secchi variable for the fableNNETAR_focal model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables.\n The model predicts this variable at the following sites: fcre, bvre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Secchi variable for the fableNNETAR_focal model. Information for the model is provided as follows: autoregressive neural net fit using the NNETAR() function in the fable R package for VERA focal variables.\n The model predicts this variable at the following sites: bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,8 +16,8 @@ "Secchi_m_sample", "Daily", "P1D", - "fcre", "bvre", + "fcre", "machine learning" ], "citation": { diff --git a/data/output/vera4cast-stac/d6d30a3a0b.json b/data/output/vera4cast-stac/d6d30a3a0b.json new file mode 100644 index 000000000..709545f02 --- /dev/null +++ b/data/output/vera4cast-stac/d6d30a3a0b.json @@ -0,0 +1,266 @@ +{ + "@context": { + "@vocab": "https://schema.org/" + }, + "@type": "Dataset", + "@id": "urn:vera4cast-stac:2b30572827", + "name": "asl.climate.window_Bloom_binary_mean_P1D_scores scores", + "description": "All scores for the Daily_Bloom_binary variable for the asl.climate.window model. Information for the model is provided as follows: Climatology, but with a rolling 10-day window for historical data. This works really well for my methane forecasts at SERC, so I thought it might be useful here.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datePublished": "2022-01-01", + "keywords": [ + "Scores", + "vera4cast", + "Biological", + "asl.climate.window", + "Bloom_binary", + "Bloom_binary_mean", + "Daily", + "P1D", + "bvre", + "fcre", + "empirical" + ], + "citation": { + "@type": "CreativeWork", + "@id": "https://doi.org/10.1002/ecs2.4686", + "url": "https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecs2.4686", + "name": "A community convention for ecological forecasting: Output files and metadata version 1.0", + "description": "This paper summarizes the open community conventions developed by the Ecological Forecasting Initiative (EFI) for the common formatting and archiving of ecological forecasts and the metadata associated with these forecasts. ", + "identifier": { + "@type": "PropertyValue", + "propertyID": "https://registry.identifiers.org/registry/doi", + "value": "doi:10.1002/ecs2.4686", + "url": "https://doi.org/10.1002/ecs2.4686" + } + }, + "variableMeasured": [ + { + "@type": "PropertyValue", + "name": "reference_datetime", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "@type": "PropertyValue", + "name": "site_id", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." + }, + { + "@type": "PropertyValue", + "name": "datetime", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "@type": "PropertyValue", + "name": "family", + "description": "For ensembles: \u201censemble.\u201d Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The \u201csample\u201d distribution is synonymous with \u201censemble.\u201d For summary statistics: \u201csummary.\u201dIf this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." + }, + { + "@type": "PropertyValue", + "name": "pub_datetime", + "description": "datetime that forecast was submitted" + }, + { + "@type": "PropertyValue", + "name": "depth_m", + "description": "depth (meters) in water column of prediction" + }, + { + "@type": "PropertyValue", + "name": "observation", + "description": "observed value for variable" + }, + { + "@type": "PropertyValue", + "name": "crps", + "description": "crps forecast score" + }, + { + "@type": "PropertyValue", + "name": "logs", + "description": "logs forecast score" + }, + { + "@type": "PropertyValue", + "name": "mean", + "description": "mean forecast prediction" + }, + { + "@type": "PropertyValue", + "name": "median", + "description": "median forecast prediction" + }, + { + "@type": "PropertyValue", + "name": "sd", + "description": "standard deviation forecasts" + }, + { + "@type": "PropertyValue", + "name": "quantile97.5", + "description": "upper 97.5 percentile value of forecast" + }, + { + "@type": "PropertyValue", + "name": "quantile02.5", + "description": "upper 2.5 percentile value of forecast" + }, + { + "@type": "PropertyValue", + "name": "quantile90", + "description": "upper 90 percentile value of forecast" + }, + { + "@type": "PropertyValue", + "name": "quantile10", + "description": "upper 10 percentile value of forecast" + }, + { + "@type": "PropertyValue", + "name": "duration", + "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" + }, + { + "@type": "PropertyValue", + "name": "model_id", + "description": "unique model identifier" + }, + { + "@type": "PropertyValue", + "name": "project_id", + "description": "unique project identifier" + }, + { + "@type": "PropertyValue", + "name": "variable", + "description": "name of forecasted variable" + } + ], + "distribution": [ + { + "@type": "DataDownload", + "contentUrl": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.climate.window.json", + "encodingFormat": "application/json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.climate.window.json\")\n\n", + "name": "Model Metadata" + }, + { + "@type": "DataDownload", + "contentUrl": "https://github.com/abbylewis/vera_meteor_strike", + "encodingFormat": "text/html", + "description": "The link to the model code provided by the model submission team", + "name": "Link for Model Code" + }, + { + "@type": "DataDownload", + "contentUrl": "s3://anonymous@bio230121-bucket01/vera4cast/scores/bundled-parquetproject_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=asl.climate.window?endpoint_override=renc.osn.xsede.org", + "encodingFormat": "application/x-parquet", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/bundled-parquetproject_id=vera4cast/duration=P1D/variable=Bloom_binary_mean/model_id=asl.climate.window?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n", + "name": "Database Access for Daily Bloom_binary" + } + ], + "spatialCoverage": { + "@type": "Place", + "geo": { + "@type": "GeoShape", + "box": "-79.8372 37.3032 -79.8159 37.3129" + }, + "additionalProperty": [ + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291041754864156672 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291043953887412224 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291044503643226112 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291045878032760832 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291047252422295552 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291047802178109440 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291051650468806656 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291052200224620544 + } + ] + } +} \ No newline at end of file diff --git a/data/output/vera4cast-stac/f9fea0ea4a.json b/data/output/vera4cast-stac/df7d35ada2.json similarity index 100% rename from data/output/vera4cast-stac/f9fea0ea4a.json rename to data/output/vera4cast-stac/df7d35ada2.json index c2c996eb3..42299c052 100644 --- a/data/output/vera4cast-stac/f9fea0ea4a.json +++ b/data/output/vera4cast-stac/df7d35ada2.json @@ -16,8 +16,8 @@ "DO_mgL_mean", "Daily", "P1D", - "bvre", "fcre", + "bvre", "machine learning" ], "citation": { diff --git a/data/output/vera4cast-stac/571f9186af.json b/data/output/vera4cast-stac/e69fa6fa11.json similarity index 99% rename from data/output/vera4cast-stac/571f9186af.json rename to data/output/vera4cast-stac/e69fa6fa11.json index 0cbd5d148..6254de6d6 100644 --- a/data/output/vera4cast-stac/571f9186af.json +++ b/data/output/vera4cast-stac/e69fa6fa11.json @@ -5,7 +5,7 @@ "@type": "Dataset", "@id": "urn:vera4cast-stac:369fac2514", "name": "climatology_Temp_C_mean_P1D_forecast forecasts", - "description": "All forecasts for the Daily_Water_temperature variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: bvre, fcre, tubr.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", + "description": "All forecasts for the Daily_Water_temperature variable for the climatology model. Information for the model is provided as follows: Historical DOY mean and sd. Assumes normal distribution.\n The model predicts this variable at the following sites: tubr, bvre, fcre.\n Forecasts are the raw forecasts that includes all ensemble members or distribution parameters. Due to the size of the raw forecasts, we recommend accessing the forecast summaries or scores to analyze forecasts (unless you need the individual ensemble members)", "datePublished": "2022-01-01", "keywords": [ "Forecasts", @@ -16,9 +16,9 @@ "Temp_C_mean", "Daily", "P1D", + "tubr", "bvre", "fcre", - "tubr", "empirical" ], "citation": { diff --git a/data/output/vera4cast-stac/fcb50023a2.json b/data/output/vera4cast-stac/fcb50023a2.json new file mode 100644 index 000000000..f5bcb7e18 --- /dev/null +++ b/data/output/vera4cast-stac/fcb50023a2.json @@ -0,0 +1,266 @@ +{ + "@context": { + "@vocab": "https://schema.org/" + }, + "@type": "Dataset", + "@id": "urn:vera4cast-stac:84a448af6a", + "name": "asl.climate.window_Temp_C_mean_P1D_scores scores", + "description": "All scores for the Daily_Water_temperature variable for the asl.climate.window model. Information for the model is provided as follows: Climatology, but with a rolling 10-day window for historical data. This works really well for my methane forecasts at SERC, so I thought it might be useful here.\n The model predicts this variable at the following sites: bvre, fcre.\n Scores are metrics that describe how well forecasts compare to observations. The scores catalog includes are summaries of the forecasts (i.e., mean, median, confidence intervals), matched observations (if available), and scores (metrics of how well the model distribution compares to observations)", + "datePublished": "2022-01-01", + "keywords": [ + "Scores", + "vera4cast", + "Physical", + "asl.climate.window", + "Water_temperature", + "Temp_C_mean", + "Daily", + "P1D", + "bvre", + "fcre", + "empirical" + ], + "citation": { + "@type": "CreativeWork", + "@id": "https://doi.org/10.1002/ecs2.4686", + "url": "https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecs2.4686", + "name": "A community convention for ecological forecasting: Output files and metadata version 1.0", + "description": "This paper summarizes the open community conventions developed by the Ecological Forecasting Initiative (EFI) for the common formatting and archiving of ecological forecasts and the metadata associated with these forecasts. ", + "identifier": { + "@type": "PropertyValue", + "propertyID": "https://registry.identifiers.org/registry/doi", + "value": "doi:10.1002/ecs2.4686", + "url": "https://doi.org/10.1002/ecs2.4686" + } + }, + "variableMeasured": [ + { + "@type": "PropertyValue", + "name": "reference_datetime", + "description": "datetime that the forecast was initiated (horizon = 0)" + }, + { + "@type": "PropertyValue", + "name": "site_id", + "description": "For forecasts that are not on a spatial grid, use of a site dimension that maps to a more detailed geometry (points, polygons, etc.) is allowable. In general this would be documented in the external metadata (e.g., alook-up table that provides lon and lat); however in netCDF this could be handled by the CF Discrete Sampling Geometry data model." + }, + { + "@type": "PropertyValue", + "name": "datetime", + "description": "datetime of the forecasted value (ISO 8601)" + }, + { + "@type": "PropertyValue", + "name": "family", + "description": "For ensembles: \u201censemble.\u201d Default value if unspecified For probability distributions: Name of the statistical distribution associated with the reported statistics. The \u201csample\u201d distribution is synonymous with \u201censemble.\u201d For summary statistics: \u201csummary.\u201dIf this dimension does not vary, it is permissible to specify family as a variable attribute if the file format being used supports this (e.g.,netCDF)." + }, + { + "@type": "PropertyValue", + "name": "pub_datetime", + "description": "datetime that forecast was submitted" + }, + { + "@type": "PropertyValue", + "name": "depth_m", + "description": "depth (meters) in water column of prediction" + }, + { + "@type": "PropertyValue", + "name": "observation", + "description": "observed value for variable" + }, + { + "@type": "PropertyValue", + "name": "crps", + "description": "crps forecast score" + }, + { + "@type": "PropertyValue", + "name": "logs", + "description": "logs forecast score" + }, + { + "@type": "PropertyValue", + "name": "mean", + "description": "mean forecast prediction" + }, + { + "@type": "PropertyValue", + "name": "median", + "description": "median forecast prediction" + }, + { + "@type": "PropertyValue", + "name": "sd", + "description": "standard deviation forecasts" + }, + { + "@type": "PropertyValue", + "name": "quantile97.5", + "description": "upper 97.5 percentile value of forecast" + }, + { + "@type": "PropertyValue", + "name": "quantile02.5", + "description": "upper 2.5 percentile value of forecast" + }, + { + "@type": "PropertyValue", + "name": "quantile90", + "description": "upper 90 percentile value of forecast" + }, + { + "@type": "PropertyValue", + "name": "quantile10", + "description": "upper 10 percentile value of forecast" + }, + { + "@type": "PropertyValue", + "name": "duration", + "description": "temporal duration of forecast (hourly = PT1H, daily = P1D, etc.); follows ISO 8601 duration convention" + }, + { + "@type": "PropertyValue", + "name": "model_id", + "description": "unique model identifier" + }, + { + "@type": "PropertyValue", + "name": "project_id", + "description": "unique project identifier" + }, + { + "@type": "PropertyValue", + "name": "variable", + "description": "name of forecasted variable" + } + ], + "distribution": [ + { + "@type": "DataDownload", + "contentUrl": "https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.climate.window.json", + "encodingFormat": "application/json", + "description": "Use `jsonlite::fromJSON()` to download the model metadata JSON file. This R code will return metadata provided during the model registration.\n \n\n### R\n\n```{r}\n# Use code below\n\nmodel_metadata <- jsonlite::fromJSON(\"https://renc.osn.xsede.org/bio230121-bucket01/vera4cast/metadata/model_id/asl.climate.window.json\")\n\n", + "name": "Model Metadata" + }, + { + "@type": "DataDownload", + "contentUrl": "https://github.com/abbylewis/vera_meteor_strike", + "encodingFormat": "text/html", + "description": "The link to the model code provided by the model submission team", + "name": "Link for Model Code" + }, + { + "@type": "DataDownload", + "contentUrl": "s3://anonymous@bio230121-bucket01/vera4cast/scores/bundled-parquetproject_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.climate.window?endpoint_override=renc.osn.xsede.org", + "encodingFormat": "application/x-parquet", + "description": "Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(\"s3://anonymous@bio230121-bucket01/vera4cast/scores/bundled-parquetproject_id=vera4cast/duration=P1D/variable=Temp_C_mean/model_id=asl.climate.window?endpoint_override=renc.osn.xsede.org\")\ndf <- all_results |> dplyr::collect()\n\n```\n \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n", + "name": "Database Access for Daily Water_temperature" + } + ], + "spatialCoverage": { + "@type": "Place", + "geo": { + "@type": "GeoShape", + "box": "-79.8372 37.3032 -79.8159 37.3129" + }, + "additionalProperty": [ + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291041754864156672 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291043953887412224 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291044503643226112 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291045878032760832 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291047252422295552 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291047802178109440 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291051650468806656 + }, + { + "@type": [ + "PropertyValue", + "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + ], + "additionalType": { + "@id": "https://stko-kwg.geog.ucsb.edu/lod/ontology#S2Cell" + }, + "name": "s2Level13", + "description": "S2 cell at level 13", + "value": 9291052200224620544 + } + ] + } +} \ No newline at end of file diff --git a/data/output/vera4cast-stac/32e4576453.json b/data/output/vera4cast-stac/ffbd6af5b2.json similarity index 100% rename from data/output/vera4cast-stac/32e4576453.json rename to data/output/vera4cast-stac/ffbd6af5b2.json index 507c5f7a4..7e6642eaa 100644 --- a/data/output/vera4cast-stac/32e4576453.json +++ b/data/output/vera4cast-stac/ffbd6af5b2.json @@ -16,8 +16,8 @@ "Secchi_m_sample", "Daily", "P1D", - "fcre", "bvre", + "fcre", "machine learning" ], "citation": {